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Abstract:

Methods, business methods, and systems are provided herein for integrated
healthcare. As the amount of medical information increases rapidly,
including information from multiple biomarkers, analysis and management
of that information becomes more and more important to extract meaningful
conclusions from the information. Statistical and computational methods
are described herein that have been created for the methods and systems
for integrated healthcare. For example, a computer system is described
extracts significance over time of PSA and fPSA biomarker tests for
prostate health.

Claims:

1-63. (canceled)

64. A method for calculating a probability of prostate cancer in a
subject, comprising: obtaining a series of at least two PSA values from a
subject; using a computer system to calculate a characteristic of said
series of PSA values, wherein said characteristic is PSA variation;
measuring a prostate volume in said subject; comparing said PSA variation
and said prostate volume to population data; and calculating a
probability of prostate cancer in said subject based on the comparison.

65. The method of claim 64, wherein the step of using a computer system
to calculate a characteristic of said series of PSA values comprises:
using a computer system to fit said series of PSA values to form a fitted
trend; and calculating said characteristic of said fitted trend, wherein
said characteristic reflects variation of said series of PSA values from
said fitted trend.

66. The method of claim 64, wherein the population data is stored in a
computer-readable medium.

67. The method of claim 64, wherein the step of calculating a probability
of prostate cancer in the subject is performed by a computer system.

68. The method of claim 64, further comprising obtaining a series of
values of a prostate biomarker from said subject, wherein calculating a
probability of prostate cancer is further based on said series of values
of said prostate biomarker.

69. The method of claim 68, wherein said prostate biomarker is free PSA.

70. The method of claim 64, wherein measuring a prostate volume in said
subject comprises measuring prostate volume in said subject at least two
times to obtain a series of prostate volumes and calculating a prostate
volume growth of said subject; and wherein comparing said prostate volume
to population data comprises comparing said prostate volume growth to
population data.

71. The method of claim 64, further comprising calculating a recommended
time for medical action, wherein said medical action is biopsy or medical
treatment.

74. A method for calculating a probability of death due to prostate
cancer by a subject, comprising: obtaining a series of at least two PSA
values from a subject; using a computer system to calculate a
characteristic of said series of PSA values, wherein said characteristic
is PSA variation; measuring a prostate volume in said subject; comparing
said PSA variation and said prostate volume to population data; and
calculating a probability of death due to prostate cancer by said subject
based on the comparison.

75. The method of claim 74, wherein the step of using a computer system
to calculate a characteristic of said series of PSA values comprises:
using a computer system to fit said series of PSA values to form a fitted
trend; and calculating a characteristic of said fitted trend, wherein
said characteristic reflects variation of said series of PSA values from
said fitted trend.

76. The method of claim 74, wherein calculating a probability of death
due to prostate cancer by said subject comprises calculating a
probability of prostate cancer in said subject.

77. The method of claim 74, further comprising calculating a recommended
time for a medical action, wherein said medical action is biopsy or a
medical treatment.

78. The method of claim 77, wherein calculating a recommended time for a
medical action comprises comparing said probability of death due to
prostate cancer with a risk of side effects of said medical action.

79. A computer system for calculating a probability of prostate cancer in
a subject, comprising: a processor suitable for performing
computer-readable instructions for: receiving a series of PSA values
measured in a subject; receiving a prostate volume measured in said
subject; calculating a characteristic of said series of PSA values,
wherein said characteristic is PSA variation; comparing said PSA
variation and said prostate volume to population data; and calculating a
probability of prostate cancer in said subject based on the comparison; a
storage unit comprising said population data in communication with said
processor; and an output device suitable for transmitting the results of
said computer-readable instructions to a user.

80. The computer system of claim 79, wherein calculating a characteristic
of said series of PSA values comprises: fitting said series of PSA values
to form a fitted trend; and calculating a characteristic of said fitted
trend, wherein said characteristic reflects variation of said series of
PSA values from said fitted trend.

81. The computer system of claim 79, wherein said processor is further
suitable for performing computer-readable instructions for calculating a
probability of death due to prostate cancer by said subject.

82. The computer system of claim 79, wherein said output device is a
device for network communication.

83. The computer system of claim 79, further comprising an input device
for receiving data taken from said subject.

Description:

CROSS-REFERENCE

[0001] This application is a continuation application of U.S. Ser. No.
12/466,684, filed on May 15, 2009 which claims the benefit of U.S.
Provisional Application No. 61/053,600, filed May 15, 2008, which
application is incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] There is increasing emphasis on disease prevention, early detection
and treatment, avoiding unnecessary treatment, timing of treatments,
avoiding invasive procedures, and reducing costs. Significant investments
are being made to accelerate discovery and use of biomarkers that
effectively detect progressing cancer. However, assaying or testing for
an individual biomarker is often not effective for detection of
progressing cancer.

[0003] The use of screening blood tests, where multiple markers are
tested, is becoming more prevalent and cost-effective. Screening for many
conditions using blood from a single draw can reduce medical costs. The
incremental cost of additional tests decreases if subsequent blood draws
are not needed. A further means of reducing costs is to store blood for
later testing if needed. New technology is also reducing the cost of
specific tests.

[0004] There is a need in the art to extract additional information from a
diagnostic test, whether it is a biomarker test or a series of biomarker
tests, or a medical image. Novel methods and systems of extracting
additional quantitative information for use by patients or physicians are
increasingly desirable to reduce the cost of medical diagnostics and
treatments and to improve the accuracy of diagnosis and efficacy of
treatments.

SUMMARY OF THE INVENTION

[0005] In an aspect, a method is disclosed of performing a course of
medical action for a medical condition of a subject comprising: obtaining
a first value of at least one biomarker from a subject; sending said
first value to a computer system that calculates a first plurality of
posterior probabilities of the occurrence of a plurality of medical
conditions of said subject using said first value, wherein said plurality
of medical conditions comprises at least a first and second medical
condition; receiving said first plurality of posterior probabilities;
performing a first course of medical action for the first medical
condition based on said first plurality of posterior probabilities;
observing a result of said first course of medical action; obtaining a
second value of at least one biomarker from said subject; sending said
second value and said result of said first course of the medical action
to said computer system that calculates a second plurality of posterior
probabilities of the occurrence of said plurality of medical conditions
of said subject, wherein said calculation uses said at second value and
said result; receiving said second plurality of posterior probabilities;
and performing a second course of medical action for the second medical
condition based on said second plurality of posterior probabilities.

[0006] In an embodiment, the first or second value is a PSA value or fPSA
value. In another embodiment, the subject is a human, for example a
patient.

[0007] In an embodiment, a computer system comprises a device for network
communication, a storage unit, and a processor. The computer system can
comprise a Monte Carlo engine.

[0008] In an embodiment, sending comprises entering said first and second
values into a webpage or using a device that transmits either or both of
said first and second values to said computer system through a wireless
network.

[0009] In an embodiment, first and second values are a first and second
biomarker trend of biomarker values over a period of time. A computer
system can calculate each of said first plurality of posterior
probabilities by relating: a prior probability of a medical condition; a
probability of observing said first biomarker trend for an individual
with said medical condition; and a probability of observing said first
biomarker trend for an individual without said medical condition. A
computer system can calculate each of said second plurality of posterior
probabilities by relating: a prior probability of a medical condition,
wherein said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability of
observing said second biomarker trend for an individual with said medical
condition; and a probability of observing said second biomarker trend for
an individual without said medical condition.

[0010] In an embodiment, a plurality of medical conditions are prostate
medical conditions, for example they can be selected from the group
consisting of the following: prostatitis due to inflammation, prostatitis
due to infection, prostate cancer, benign prostate hyperplasia, and no
prostate disease.

[0011] In an embodiment, receiving comprises viewing a display of said
posterior probabilities, for example a display on an output device. An
output device can be selected from the group consisting of the following:
a computer, a webpage, an electronic medical record, a printout, and a
personal electronic device.

[0012] In an embodiment, a first or second course of medical action is
delivering medical treatment to said subject, such as a medical treatment
is selected from a group consisting of the following: a pharmaceutical,
surgery, organ resection, and radiation therapy. In an embodiment, a
pharmaceutical comprises a chemotherapeutic compound for cancer therapy.
In another embodiment, the first or second course of medical action
comprises administration of medical tests or medical imaging of said
subject or setting a specific time for delivering medical treatment or a
biopsy or a consultation with a medical professional.

[0013] In another aspect, a business method is disclosed that comprises:
receiving a first value of at least one biomarker of a subject;
calculating a first plurality of posterior probabilities of the
occurrence of a plurality of medical conditions of said subject with a
computer system using said a first value; delivering said first plurality
of posterior probabilities to a user; receiving a second value of at
least one biomarker of a subject and a result of a course of medical
action taken by said user based upon said delivery of said first
plurality of posterior probabilities; calculating a second plurality of
posterior probabilities of the occurrence of a plurality of medical
conditions of said subject with said computer system using said a second
value and said result of a course of the medical action; and delivering
said second plurality of posterior probabilities to said user. In an
embodiment, the first or second values are received from a user, such as
a user selected from the group consisting of the following: a physician,
a health care provider, a pharmacy, an insurance company, and the
subject. A first or second value can also be received from said user
through a webpage or an electronic device or an assay device.

[0014] In another embodiment, the first or second values are received from
a device, such as a device selected from the group consisting of the
following: a lab test device, a point-of-care assay device, a personal
electronic device, an electronic medical record, and a computer system.

[0015] Calculating can be carried out by a Monte Carlo engine and can be a
Bayesian statistical calculation.

[0016] In an embodiment, a plurality of medical conditions is at least
four medical conditions, for example from the group consisting of:
prostatitis due to inflammation, prostatitis due to infection, prostate
cancer, benign prostate hyperplasia, and no prostate disease. A biomarker
value can be from a PSA or fPSA assay.

[0017] A result of a course of medical action can be selected from the
group consisting of the following: a test result, a diagnosis, a cure, an
effect, and no effect. Posterior probabilities can be delivered to a user
through an electronic medical record or a webpage or an electronic device
with a display or a printout.

[0018] In an embodiment, the computer system comprises a processor, a
storage unit, and a device for network communication.

[0019] In an embodiment, a business method is carried out for a fee, for
example each delivery of posterior probabilities is carried out for a
fee.

[0020] A business method can further comprise suggesting a course of
medical action to said user based on said posterior probabilities, and
the suggestion can be provided for a fee.

[0021] In an aspect of the invention, a method of delivering a probability
that a subject has a medical condition to a user comprises: calculating a
plurality of posterior probabilities of the occurrence of a plurality of
medical conditions of a subject having a biomarker trend, wherein said
biomarker trend comprises biomarker values from said subject at more than
one time, and wherein each of said plurality of posterior probabilities
is calculated by relating: a prior probability of the occurrence of each
of said plurality of medical conditions; and a probability of observing
said biomarker trend for an individual with each medical condition; and a
probability of observing said biomarker trend for an individual without
each medical condition; and delivering said plurality of probabilities of
said plurality of medical conditions to a user with an output device.

[0022] In another aspect, a method of delivering a probability that a
subject has a medical condition to a user comprises: calculating a
plurality of posterior probabilities of the occurrence of a plurality of
medical conditions of a subject having a result of a course of medical
action and having a biomarker trend, wherein said biomarker trend
comprises biomarker values from said subject at more than one time, and
wherein each of said plurality of posterior probabilities is calculated
by relating: a prior probability of the occurrence of each of said
plurality of medical conditions, wherein said prior probability was
calculated using subject information comprising said result of a course
of medical action; a probability of said biomarker trend for an
individual with each medical condition; and a probability of said
biomarker trend for an individual without each medical condition; and
delivering said plurality of probabilities of said plurality of medical
conditions to a user with an output device. A biomarker trend can be a
PSA trend or fPSA trend.

[0023] In an embodiment, an output device is selected from the group
consisting of the following: a computer, a webpage, an electronic medical
record, a printout, and a personal electronic device.

[0024] A course of medical action can be delivering medical treatment to
said subject, for example a medical treatment selected from a group
consisting of the following: a pharmaceutical, surgery, organ resection,
and radiation therapy.

[0025] The course of medical action can also comprise administration of
medical tests, medical imaging of said subject, setting a specific time
for delivering medical treatment, a biopsy, and/or consultation with a
medical professional.

[0026] In yet another aspect, a method of delivering a probability that a
subject has a medical condition to a user is disclosed comprising:
calculating a plurality of posterior probabilities of the occurrence of a
plurality of prostate medical conditions of a subject having a PSA value
and an fPSA value, each at more than one time thereby having a PSA trend
and an fPSA trend, wherein each of said plurality of posterior
probabilities is calculated by relating: a prior probability of a
prostate medical condition; and a probability of observing said PSA trend
and said fPSA trend for an individual with said prostate medical
condition; and a probability of observing said PSA trend and said fPSA
trend for an individual without said prostate medical condition; and
delivering said plurality of probabilities of said plurality of medical
conditions to a user with an output device. In an embodiment, a method
can further comprise: calculating a second plurality of posterior
probabilities of the occurrence of said plurality of prostate medical
conditions of a subject having a result of a course of medical action and
having a new PSA value and a new fPSA value, each at more than one time
thereby having a second PSA trend and a second fPSA trend, wherein each
of said plurality of posterior probabilities is calculated by relating: a
prior probability of a prostate medical condition, wherein said prior
probability was calculated using subject information comprising said
result of a course of medical action; and a probability of observing said
second PSA trend and said second fPSA trend for an individual with said
prostate medical condition; and a probability of observing said second
PSA trend and said second fPSA trend for an individual without said
prostate medical condition; and delivering said second plurality of
probabilities of said plurality of medical conditions to the user with an
output device.

INCORPORATION BY REFERENCE

[0027] All publications mentioned in this specification are herein
incorporated by reference to the same extent as if each individual
publication was specifically and individually indicated to be
incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Many features of the invention are set forth with particularity in
the appended claims. A better understanding of the features and
advantages of the invention will be obtained by reference to the
following detailed description that sets forth illustrative embodiments,
in which many of the invention are utilized, and the accompanying
drawings of which:

[0037]FIG. 9 demonstrates an embodiment of the personalized probability
distributions and probabilities module uses a four dimensional frequency
generator.

[0038] FIG. 10 shows an embodiment of a four dimensional frequency
generator that calculates personalized probability distributions and
probabilities for the no cancer case in iterative fashion.

[0039] FIG. 11 shows an embodiment of a four dimensional frequency
generator that calculates personalized probability distributions and
probabilities for cancer plus no cancer cases in iterative fashion.

[0040]FIG. 12 shows an example of the 100 possible buckets of possible
results when each dimension is divided into ten segments.

[0041]FIG. 13 shows an example of the 10,000 possible buckets of possible
results when each dimension is divided into ten segments (even though
only three of the four dimensions can be depicted).

[0042]FIG. 14 shows conceptually the bucket of concern defined by the
range of PSA and PSAV results around the observed trend results.

[0043] FIG. 15 suggests conceptually the hypercube bucket of concern
defined by the range of PSA, PSAV, fPSA % and fPSAV % results around the
observed trend results (even though only three of the four dimensions can
be depicted).

[0044]FIG. 16 shows an exemplary four dimensional frequency generator for
the no cancer case. Each iteration is initiated by the Monte Carlo
iteration controller.

[0045] FIG. 17 shows an exemplary Monte Carlo process for generating
outcomes for year X cancer from a number of probability distributions,
where X is a measure of cancer progression.

[0052]FIG. 24 shows a calculation of the probability of progressing
cancer as a function of window size.

[0053]FIG. 25 illustrates the results of an exemplary linear function for
estimating the PSA trend.

[0054]FIG. 26 and FIG. 27 show how variable trend functions and window
sizes can be added to the no cancer four dimensional frequency generator
and the cancer plus no cancer four dimensional frequency generator.

[0055] FIG. 28 shows an example of this triangle weighting function as a
function of PSA.

[0056]FIG. 29 shows an example of a weighting function as a function of
PSA and PSA velocity.

[0065]FIG. 39 shows how probability distributions of each prostate
condition can be affected by past medical experience with the conditions,
and the results of imaging, tests, treatment and other medical
procedures.

[0066]FIG. 40 shows, for example, a negative bacterial culture and no
impact from antibiotic treatment may reduce the probability of infection
prostatitis and increase the probability of inflammation and the
probability of other conditions.

[0067]FIG. 41 shows an embodiment of other clinical conditions PSA
increment is the product of the other conditions leak rate increment,
drawn from the other conditions LI % distribution, and trend PSA from the
PSA module.

[0068]FIG. 42 shows an example of how the probability of the presence of
infection (P %) for a man tends to increase with age and past history of
infection.

[0071]FIG. 45 shows how the probability of each of these three benign
conditions can change over time for a man.

[0072]FIG. 46 shows an embodiment of how four similar Bayes processes are
used to calculate the probability of the prostate conditions: volume
growth due to BPH, inflammation prostatitis, infection prostatitis and
progressing cancer.

[0073]FIG. 47 shows the status of the prostate is partitioned into 16
different condition combinations that are composed of five different
prostate conditions.

[0074]FIG. 48, FIG. 49, and FIG. 50 show an aspect of the invention, a
probability generator for all prostate conditions consolidates output
from five exemplary separate probability generators for a healthy
prostate and the four prostate that include without limitation: volume
growth due to BPH, inflammation prostatitis, infection prostatitis and
progressing cancer.

[0075] FIG. 51 shows the probability distributions of each prostate
condition can be affected by past experience and the results of imaging,
tests, treatment and other medical procedures as shown in.

[0076]FIG. 52 shows an embodiment of the no cancer probability
generators.

[0077]FIG. 53 shows an embodiment of a healthy prostate module that has
three distributions for Monte Carlo draws: Vol, PSAD and fPSA %.

[0078]FIG. 54 shows an embodiment of a BPH volume growth module that has
four distributions for Monte Carlo draws: Vol, VolVel, PSAD and fPSA %.

[0079]FIG. 55 shows an embodiment of an inflammation prostatitis module
that has three distributions for Monte Carlo draws: L %, LV % and fPSA %.

[0080]FIG. 56 shows an embodiment of an infection prostatitis module that
has three distributions for Monte Carlo draws: L %, LV % and fPSA %.

[0081] FIG. 57 shows an embodiment of a first step of a tuning process of
the invention that is to tune the no cancer static distribution for a
given age (t=0), such as age 55.

[0083]FIG. 59 and FIG. 60 show embodiments of a second step that is to
tune velocity parameters to achieve the no cancer static distribution for
ten years later (t=10), such as age 65.

[0084]FIG. 61 illustrates an exemplary computer system of the invention
comprising a plurality of graphical user interfaces, a front end server
comprising databases, and a back end server capable of performing
calculations of probabilities.

[0085] FIG. 62 illustrates an exemplary method of delivering a probability
that a subject has a medical condition to a user and using the
probability to take a course of medical action.

[0086]FIG. 63 and FIG. 64 illustrate exemplary courses of events related
to a method or system of the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0087] Methods, business methods, and systems are provided herein for
integrated healthcare. As the amount of medical information increases
rapidly, including information from multiple biomarkers, analysis and
management of that information becomes more and more important to extract
meaningful conclusions from the information. Methods and systems, as
described herein, provide calculations of biomarker values into useful
analytical data for a user. The methods and systems have potential in a
variety of healthcare cases, including genomics, diagnosis, point-of-care
applications, pharmaceuticals, and clinic trials. For the purpose of
example, many of the methods and systems are described herein in the
context of analyzing data from men regarding prostate medical conditions.

[0088] In an aspect, a method utilizes computer-implemented personalized
probability determination systems. In another aspect, the invention
features methods for use in integrated health systems and methods related
to organs of the human body and to cancer.

[0089] A treatment timing system can help men and their medical advisers
choose a time for treatment of prostate cancer. The Treatment Timing
system can build on the results of personalized probability analysis. The
timing of treatment for prostate cancer can be a balancing act. Early
treatment often increases the chance of cure but may increase the risk of
unnecessary treatment and side effects.

Timing System

[0090] Exemplary methods and systems are introduced briefly herein along
with a flow chart on FIG. 1 as described here. The Probabilities and
Early Warning results from dynamic screening are an input to the
Treatment Timing system. Other relevant information including personal
profile information is entered. Treatment is selected for analysis by the
user or treatments are analyzed in iterative fashion by the system. The
system analyzes a range of years of early and late warning in iterative
fashion. The annual probability of treatment for each future year is
projected based on the current probability of progressing cancer and
years of early warning from the dynamic screening system in step. The
Cancer Cure Ratio is estimated for treatment each year based on the
amount of early or late warning. The Cure Ratio is used to project the
probability of recurrence after treatment over time and subsequent
progression. The probability of death from prostate cancer is projected
from the risk of subsequent progression for each year of potential
treatment and then cumulated for an overall probability projection. The
risk of death from other causes is considered in estimating the increase
in the overall risk of death for each future year. For each year of
treatment the probability of treatment in that year is used to weight the
subsequent risk of side effects. The risks for each year of treatment are
cumulated to estimate an overall risk of side effects for each future
year. Changes in Life Score are calculated for the increased risk of
death by year and for the risk of side effects using the Emotional
Weights entered by the user in his Personal profile. The man's overall
Life Score may be reduced by the Life Score Impacts of increased risks of
death and side effects. Results are summarized for each strategy. A man,
medical personnel and other users (for example, family) can use Life
Score simulations to help them choose the best timing for biopsy and
treatment of progressing cancer. For a biopsy, a doctor uses a device to
inject thin hollow needles into the prostate to extract tissue.
Typically, a pathologist examines the tissue and may provide a diagnosis
of prostate cancer. Primary treatment is intended to cure prostate cancer
and can include surgery to remove the prostate and various types of
radiation to kill the cancer. A pathology report after surgery can
provide useful information about the progress of cancer.

[0091] An exemplary life outcome simulator, as shown on FIG. 2, can be
used to calculate Life Score Impacts and Life Scores on FIG. 1 for a
range of treatment timing scenarios. The probability of progressing
cancer from a previous module is an example input. The user may supply
information on his Personal profile. The system may supply a standard
range of treatment timing scenarios.

[0092] In an embodiment, Life Score is a measure of well-being and length
of life, based on the information entered in the profile. The exemplary
Life Score graph on FIG. 3 shows how Life Score varies for a range of
treatment timing. A value of 100% may represent Life Score in the absence
of prostate cancer and serve as a point of reference. In the example of
FIG. 4, The Life Score curve is relatively flat because timing of
prostate cancer treatment causes relatively small changes in well-being
and length of life. Timing can be measured in years before and after the
Transition Point, (for example, the time of progression when the cure
rate begins to decline steeply) of progressing cancer (year 0). Before
the Transition Point the Cure Ratio may decline relatively slowly. After
the Transition Point the Cure Ratio can drop more steeply as the risk
increases that cancer has spread outside of the prostate.

[0093] The line and treatment diamond on the graph on FIG. 3 may depend on
the primary treatment selected in the profile (for example, surgery, dual
radiation, seed radiation and external radiation). The treatment diamond
on each graph shows the treatment timing that maximizes Life Score and
minimizes Life Score impact. For Life Scores that are different, one way
to interpret the difference can be in the context of a total life. For
example, if someone expects to live thirty more years, a 3% difference in
Life Score would be equivalent to almost 1 year of life. In the exemplary
figures, the diamond on each graph shows a rough estimate of biopsy
timing that corresponds with the treatment timing that maximizes Life
Score. A first biopsy should occur roughly six months to a year before
the optimal time for treatment, so the biopsy timing diamond may show up
on the graphs approximately six months to a year or more before the
treatment timing diamond. The actual size of the biopsy lead time depends
on a variety of factors.

[0094] In an embodiment, Life Score Impact is the reduction in Life Score
by side effects and death from prostate cancer. Life Score Impact can
measure the drop from 100% on the Life Score graph of the previous FIG.
3. The graph on FIG. 4 shows an exemplary Life Score Impact for the range
of treatment timing. The bottom curve shows the total Life Score Impact
for the treatment that is chosen. It is the sum of reduction in Life
Score from side effects and death from prostate cancer. The curve is more
pronounced than on the previous graph because the scale has been
expanded. It does not span the full range of possible impacts from 0% to
100%. The treatment diamond on each graph shows the treatment timing that
maximizes Life Score and minimizes Life Score impact. The diamond on each
graph shows a rough estimate of biopsy timing that corresponds with the
treatment timing that maximizes Life Score. The top curve shows the Life
Score Impact of all side effects. The impact is greatest on the left when
the risk of unnecessary treatment is greatest. The middle curve shows the
Life Score Impact of death from prostate cancer. The impact is greatest
on the right when late treatment leads to a decrease in cure rate and an
increased risk of cancer death.

[0095] Disclosed herein are computer-implemented personalized
probabilities determination systems and methods for use in integrated
health systems and methods related to organs of the human body and to
cancer. For example, a system and method is disclosed herein for
estimating trends in biomarkers and calculating the probability of
certain conditions of one or more organs of the human body. This
exemplary system and method could be used for any condition of any organ
of the human body. An application to the male prostate with a focus on
progressing prostate cancer is disclosed as an example here without
limitation.

Personalized Probabilities

[0096] In an aspect, a method is disclosed of performing a course of
medical action for a medical condition of a subject comprising: obtaining
a first value of at least one biomarker from a subject;

[0097] sending said first value to a computer system that calculates a
first plurality of posterior probabilities of the occurrence of a
plurality of medical conditions of said subject using said first value,
wherein said plurality of medical conditions comprises at least a first
and second medical condition; receiving said first plurality of posterior
probabilities; performing a first course of medical action for the first
medical condition based on said first plurality of posterior
probabilities; observing a result of said first course of medical action;
obtaining a second value of at least one biomarker from said subject;
sending said second value and said result of said first course of the
medical action to said computer system that calculates a second plurality
of posterior probabilities of the occurrence of said plurality of medical
conditions of said subject, wherein said calculation uses said at second
value and said result; receiving said second plurality of posterior
probabilities; and performing a second course of medical action for the
second medical condition based on said second plurality of posterior
probabilities.

[0098] In an embodiment, the first or second value is a PSA value or fPSA
value. In another embodiment, the subject is a human, for example a
patient.

[0099] In an embodiment, a computer system comprises a device for network
communication, a storage unit, and a processor. The computer system can
comprise a Monte Carlo engine.

[0100] In an embodiment, sending comprises entering said first and second
values into a webpage or using a device that transmits either or both of
said first and second values to said computer system through a wireless
network.

[0101] In an embodiment, first and second values are a first and second
biomarker trend of biomarker values over a period of time. A computer
system can calculate each of said first plurality of posterior
probabilities by relating: a prior probability of a medical condition; a
probability of observing said first biomarker trend for an individual
with said medical condition; and a probability of observing said first
biomarker trend for an individual without said medical condition. A
computer system can calculate each of said second plurality of posterior
probabilities by relating: a prior probability of a medical condition,
wherein said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability of
observing said second biomarker trend for an individual with said medical
condition; and a probability of observing said second biomarker trend for
an individual without said medical condition.

[0102] In an embodiment, a plurality of medical conditions are prostate
medical conditions, for example they can be selected from the group
consisting of the following: prostatitis due to inflammation, prostatitis
due to infection, prostate cancer, benign prostate hyperplasia, and no
prostate disease.

[0103] In an embodiment, receiving comprises viewing a display of said
posterior probabilities, for example a display on an output device. An
output device can be selected from the group consisting of the following:
a computer, a webpage, an electronic medical record, a printout, and a
personal electronic device.

[0104] In an embodiment, a first or second course of medical action is
delivering medical treatment to said subject, such as a medical treatment
is selected from a group consisting of the following: a pharmaceutical,
surgery, organ resection, and radiation therapy. In an embodiment, a
pharmaceutical comprises a chemotherapeutic compound for cancer therapy.
In another embodiment, the first or second course of medical action
comprises administration of medical tests or medical imaging of said
subject or setting a specific time for delivering medical treatment or a
biopsy or a consultation with a medical professional.

[0105] In an aspect of the invention, a method of delivering a probability
that a subject has a medical condition to a user comprises: calculating a
plurality of posterior probabilities of the occurrence of a plurality of
medical conditions of a subject having a biomarker trend, wherein said
biomarker trend comprises biomarker values from said subject at more than
one time, and wherein each of said plurality of posterior probabilities
is calculated by relating: a prior probability of the occurrence of each
of said plurality of medical conditions; and a probability of observing
said biomarker trend for an individual with each medical condition; and a
probability of observing said biomarker trend for an individual without
each medical condition; and delivering said plurality of probabilities of
said plurality of medical conditions to a user with an output device.

[0106] In another aspect, a method of delivering a probability that a
subject has a medical condition to a user comprises: calculating a
plurality of posterior probabilities of the occurrence of a plurality of
medical conditions of a subject having a result of a course of medical
action and having a biomarker trend, wherein said biomarker trend
comprises biomarker values from said subject at more than one time, and
wherein each of said plurality of posterior probabilities is calculated
by relating: a prior probability of the occurrence of each of said
plurality of medical conditions, wherein said prior probability was
calculated using subject information comprising said result of a course
of medical action; a probability of said biomarker trend for an
individual with each medical condition; and a probability of said
biomarker trend for an individual without each medical condition; and
delivering said plurality of probabilities of said plurality of medical
conditions to a user with an output device. A biomarker trend can be a
PSA trend or fPSA trend.

[0107] In an embodiment, an output device is selected from the group
consisting of the following: a computer, a webpage, an electronic medical
record, a printout, and a personal electronic device.

[0108] A course of medical action can be delivering medical treatment to
said subject, for example a medical treatment selected from a group
consisting of the following: a pharmaceutical, surgery, organ resection,
and radiation therapy.

[0109] The course of medical action can also comprise administration of
medical tests, medical imaging of said subject, setting a specific time
for delivering medical treatment, a biopsy, and/or consultation with a
medical professional.

[0110] In yet another aspect, a method of delivering a probability that a
subject has a medical condition to a user is disclosed comprising:
calculating a plurality of posterior probabilities of the occurrence of a
plurality of prostate medical conditions of a subject having a PSA value
and an fPSA value, each at more than one time thereby having a PSA trend
and an fPSA trend, wherein each of said plurality of posterior
probabilities is calculated by relating: a prior probability of a
prostate medical condition; and a probability of observing said PSA trend
and said fPSA trend for an individual with said prostate medical
condition; and a probability of observing said PSA trend and said fPSA
trend for an individual without said prostate medical condition; and
delivering said plurality of probabilities of said plurality of medical
conditions to a user with an output device. In an embodiment, a method
can further comprise: calculating a second plurality of posterior
probabilities of the occurrence of said plurality of prostate medical
conditions of a subject having a result of a course of medical action and
having a new PSA value and a new fPSA value, each at more than one time
thereby having a second PSA trend and a second fPSA trend, wherein each
of said plurality of posterior probabilities is calculated by relating: a
prior probability of a prostate medical condition, wherein said prior
probability was calculated using subject information comprising said
result of a course of medical action; and a probability of observing said
second PSA trend and said second fPSA trend for an individual with said
prostate medical condition; and a probability of observing said second
PSA trend and said second fPSA trend for an individual without said
prostate medical condition; and delivering said second plurality of
probabilities of said plurality of medical conditions to the user with an
output device.

[0111] A system to perform the Bayes calculation of the probability of
progressing cancer can be configured with the following components: 1)
prior probabilities of cancer at various stages of progression; 2)
probability of the observation of various biomarker trends conditional on
no progressing cancer; and 3) probability of the observation of various
biomarker trends conditional on cancer at various stages of progression.

[0112] A system can be configured for generating one or both of the last
two categories of probabilities for an individual man with specific
observed biomarker trends and corresponding measurement uncertainty in
those trends.

[0113] For example, consider a man concerned about prostate cancer with a
series of PSA and free PSA biomarker results from blood tests. Trends can
be estimated for each biomarker and analyzed using methods previously
disclosed. For example, trend PSA velocity is the annual rate of change
in trend PSA; trend free PSA % is trend free PSA divided by trend PSA;
and trend free PSA velocity % is trend free PSA velocity divided by trend
PSA velocity. The results can be as in Table 1.

[0114] Other information about the man may be available, including, age,
measurement of prostate volume in some cases, and other factors that may
affect the conditional probabilities.

[0115] Typically, no highly specific conditional distributions can be
estimated directly from available population data. In an aspect, a
disclosed method calculates the needed personalized probabilities.

[0116] In an embodiment, a method comprises creating personalized biologic
probability models of several states: 1) no cancer conditions of the
prostate: healthy and volume growth; 2) cancer at various stages of
progression, and 3) combined models of no cancer conditions and various
stages of cancer progression. Those models are then combined with trend
uncertainty models to create an overall multi-dimensional distribution or
part of the distribution relevant to the specific trend results. The
distributions can be multi-dimensional in that trend values and trend
velocities, or annual rates of change, are considered for at least one
biomarker, such as PSA. The disclosed example describes a method for
creating four dimensional distributions and probabilities for two
biomarkers: PSA and free PSA. In an embodiment, higher dimensional
distributions and probabilities are needed when additional biomarkers are
considered.

[0117] For example, Monte Carlo methods may be used to create four
dimensional probability distributions for PSA, PSAV, fPSA % and fPSAV %
from random draws from the probability distributions of the underlying
biologic and trend uncertainty models. A calculation process can be time
consuming and slow a response user inputting and receiving information on
the internet or world wide web. The complexity and time of calculation
can increase exponentially as additional biomarkers become available and
are incorporated into the method. Therefore, efficient methods of
calculating the probabilities can be beneficial.

[0118] For example, a method focuses on the probabilities of the observed
trend values rather than larger four dimensional probability
distributions for PSA, PSAV, fPSA % and fPSAV % for the full range of
possible outcomes. This approach reduces the amount of calculations
necessary to calculate the personalized probabilities needed for the
Bayes calculations. In an embodiment, the reduction is achieved in
practice using a hierarchical triage approach that aborts a Monte Carlo
iteration as soon as one of the values falls outside the target range for
first PSA, then PSAV, then fPSA % and finally fPSAV %.

[0119] A prostate dynamic screening system can help men and their doctors
screen for progressing cancer, long-term conditions and short-term
conditions. It provides early warning of progressing cancer while
reducing the probability of unnecessary treatment and side effects. The
results can be useful inputs to the optimal Treatment Timing system. The
prostate is the organ of the body used in many of the examples described
herein, however, the methods and systems described herein are useful for
a variety of biomarkers for a variety of diseases. Conditions used as
examples are progressing prostate cancer, prostate volume growth caused
by Benign Prostatic Hyperplasia (BPH) and infections of the prostate.
Both PSA and free PSA tests can be used for screening. Other tests may
supplement them or replace them.

[0120] The flow chart on FIG. 5 provides an exemplary overview of an
embodiment of a dynamic screening system. For one person, biomarker and
image results are input on the left. For the prostate, these are PSA and
free PSA test results and ultrasound measurements of prostate volume. The
experience of other men is input from the top. A diagnosis of temporary
conditions comes out the bottom. For the prostate, an infection is the
most common and serious temporary condition. Diagnoses of progressing
cancer and long-term conditions (volume growth due to BPH for the
prostate) are output on the right. All output becomes part of all
screening history and is fed back as the experience of other men to
increase the power of dynamic screening.

[0121] The flow chart on FIG. 6 shows some embodiments of modules of the
dynamic screening system. A user can complete a profile. The prostate
strategy system can analyze strategy alternatives and can choose the best
life strategy.

[0122] Using the dynamic screening system, the man can follow suggestions
about the type and timing of primary and secondary screening tests.
Typically the system can recommend a baseline prostate volume study and
annual PSA and free PSA tests. Free PSA tests are currently recommended;
however, other tests may be recommended in the future in conjunction with
free PSA or to substitute for it. Tests results can be entered into the
system for analysis and guidance. Steadily increasing PSA due to prostate
enlargement from BPH, if rapid enough, may lead the system to suggest
periodic prostate volume measurements to define the rate of growth. Tests
results can be entered into the system for analysis and guidance.

[0123] The dynamic screening system can recognize the false alarms caused
by infection and other temporary conditions, provide a calming
perspective, suggest new PSA and free PSA tests after the infection or
condition has passed, and analyze the results of new tests.

[0124] The dynamic screening system can recognize early warning of
possible cancer progression and suggest additional confirmation tests.
Confirmation tests may include other components of PSA such as Pro PSA
and any other useful new markers developed in the future. In addition, a
new prostate volume study may be suggested, perhaps using more expensive
technology if rapid prostate enlargement is a factor. A second round of
confirmation tests can be suggested--perhaps six months after the first.
Additional confirmation tests can be suggested until progression has been
confirmed or rejected.

[0125] The dynamic screening system can confirm a high probability of
progressing cancer when its calculation shows the probability is high
enough to warrant consideration of biopsy and treatment

[0126] The optimal treatment timing system can calculate the optimal
schedule for biopsy and treatment based on ongoing screening tests and
the information entered in the profile. The man and his advisors can use
the results to schedule a first biopsy and subsequent treatment.

[0127] In the feedback learning process, the man or his doctor will
provide follow up information for the system to analyze and incorporate
for use by other men.

[0128] The exemplary long-term probabilities module of FIG. 6 estimates
the probabilities of one or more long-term conditions, such as
progressing cancer or prostate volume growth. FIG. 7 shows an example of
the high level inputs and outputs for estimating the probability of
progressing cancer. Prior probabilities are the starting point in FIG. 6.
Trend residual velocities come from FIG. 6. Velocities and trends may be
used in other embodiments. The long-term probabilities module on FIG. 7
adjusts the prior probabilities of progressing cancer based on how the
trend residual velocities compare with patterns for progressing cancer
and the predicted values for no cancer. A variety of methods can be used
to estimate the probability, including Bayesian and simulation methods.
The process can involve a variety of cancer stages, characterized by
years of early warning, which is measured as years before the transition
point, defined as the time of progression when the cure rate begins to
decline steeply. Therefore, a module may consider a range of progressing
cancer possibilities (different years of early warning) and a no-cancer
(not present or not progressing) possibility defined by the no-cancer
predicted values. For each of these possibilities a probability
distribution can be constructed that can be characterized by a mean and
by variation, which can be characterized by standard deviations. There
are two sources of variation that can be considered. First, trend
variation can be caused by possibly random variation in test results.
Second, biologic variation can be caused by differences among men or for
a specific man over time.

[0129] The approaches described herein can be used as an alternative
method for creating the long-term probabilities, as shown on FIG. 8. The
long-term probabilities module is split into a personalized probability
distributions module and probabilities module and a Bayes long-term
probabilities module. The Bayes calculations in the second module have
been disclosed in the above incorporated references. The first module is
described below. The outputs of module are probabilities of the observed
trend results: PSA, PSAV, fPSA % and fPSAV % conditional on no cancer and
cancer for various years (X). These are created using personal
information and input from biologic and trend models, as disclosed below.

[0130] In an embodiment, the personalized probability distributions and
probabilities module uses a four dimensional frequency generator, shown
on FIG. 9, which calculates personalized probability distributions and
probabilities for cancer and no cancer cases in iterative fashion. Each
iteration is initiated by the Monte Carlo iteration controller and ended
by the Monte Carlo iteration completion module, which returns control to
the controller. For each iteration, trend values for a healthy prostate
are generated from probability distributions. Trend values for prostate
volume growth are generated from probability distributions. No cancer
values are calculated in module as the sum of values. The values for each
iteration are added to the appropriate four dimensional bucket defined by
ranges in four dimensions. As the number of iterations increase,
frequency distributions for the no cancer case are built up and output at
the end of the process. For each iteration, trend values for each year X
cancer case are generated from probability distributions. A range of
cases are calculated for year X cancers, where X is a measure of cancer
progression. Values for each year X cancer plus no cancer case are
calculated in module as the sum of values. The values for each iteration
are added to the appropriate four dimensional bucket defined by ranges in
four dimensions. As the number of iterations increase, frequency
distributions for each year X cancer plus no cancer case are built up and
output at the end of the process.

[0131] It can be computationally more efficient to use independent Monte
Carlo processes for the no cancer case and cancer plus no cancer cases.
In an embodiment, the four dimensional frequency generator, shown on FIG.
10, calculates personalized probability distributions and probabilities
for the no cancer case in iterative fashion. Each iteration is initiated
by the Monte Carlo iteration controller and ended by the Monte Carlo
iteration completion module, which returns control to the controller. For
each iteration, trend values for a healthy prostate are generated from
probability distributions. Trend values for prostate volume growth are
generated from probability distributions. No cancer values are calculated
as the sum of values. The values for each iteration are added to the
appropriate four dimensional bucket defined by ranges in four dimensions.
As the number of iterations increase, frequency distributions for the no
cancer case are built up.

[0132] In another embodiment, the four dimensional frequency generator,
shown on FIG. 11, calculates personalized probability distributions and
probabilities for cancer plus no cancer cases in iterative fashion. Each
iteration is initiated by the Monte Carlo iteration controller and ended
by the Monte Carlo iteration completion module, which returns control to
the controller. For each iteration, trend values for a healthy prostate
are generated from probability distributions. Trend values for prostate
volume growth are generated from probability distributions. No cancer
values are calculated as the sum of values. For each iteration, trend
values for each year X cancer case are generated from probability
distributions. A range of cases are calculated for year X cancers, where
X is a measure of cancer progression. Values for each year X cancer plus
no cancer case are calculated in module as the sum of values from. The
values for each iteration are added to the appropriate four dimensional
bucket defined by ranges in four dimensions. As the number of iterations
increase, frequency distributions for each year X cancer plus no cancer
case are built up.

[0133] The approach described in this example generates extensive four
dimensional distributions that can be used to find the probabilities
needed for the Bayes calculations of the probability of progressing
cancer. However, the calculations can be time consuming and cause delays
in real-time responses to users. The approach of focused probabilities is
discussed below to address this if it is an issue for a situation at
hand. The number of calculations and the time to perform them can be
reduced substantially by focusing narrowly on the probabilities needed
for the Bayes calculations rather than on generating extensive four
dimensional distributions. Detailed methods for focusing on the needed
probabilities are disclosed below.

[0134] For an exemplary biomarker, such as PSA, two dimensions may be
needed, for example, PSA and PSA velocity (PSAV). A two dimensional
rectangle of possible Monte Carlo results can be created by dividing each
dimension into segments. The example in FIG. 12 shows the 100 possible
buckets of possible results when each dimension is divided into ten
segments.

[0135] As an example, the segments for each of the two dimensions can be
as described in Table 2 and 3.

[0136] In another example, for two tests, such as PSA and free PSA, four
dimensions can be important, for example PSA, PSAV, fPSA % and fPSAV %. A
four dimensional hyper cube of possible Monte Carlo results can be
created by dividing each dimension into segments. The example of FIG. 13
suggests the 10,000 possible buckets of possible results when each
dimension is divided into ten segments (even though only three of the
four dimensions can be depicted).

[0137] In another example, consider a man concerned about prostate cancer
with a series of PSA biomarker results from blood tests. Trends can be
estimated for each biomarker and analyzed using methods previously
disclosed. For example, the results can be as described in Table 4.

The gray rectangle on the table of FIG. 14 shows conceptually the bucket
of concern defined by the range of PSA and PSAV results around the
observed trend results. For one case, other buckets that are not shaded
are not of interest.

[0138] For a man concerned about prostate cancer with a series of PSA and
free PSA biomarker results from blood tests, trends can be estimated for
each biomarker and analyzed using methods previously disclosed. The
results can be as in Table 6.

[0140] The small cube inside the large cube shown by the example in FIG.
15 suggests conceptually the hypercube bucket of concern defined by the
range of PSA, PSAV, fPSA % and fPSAV % results around the observed trend
results (even though only three of the four dimensions can be depicted).
For one case, the other buckets that are outside the small cube are not
of interest. In general, for a single case trend values for PSA, PSAV,
fPSA % and fPSAV % are known, which is a point in the 4D hyper cube. A
small hyper cube bucket around the point can be created to collect Monte
Carlo results that fall within the ranges. The frequency of the results
in the bucket can be used to estimate the probability of the results.

Monte Carlo results that fall with the bucket, like the solid dot in the
small cube of FIG. 15, are recorded; and results that fall outside the
bucket, like the circle in the large cube of FIG. 15, are not recorded.

[0141]FIG. 16 shows an exemplary four dimensional frequency generator for
the no cancer case. Each iteration is initiated by the Monte Carlo
iteration controller. For each iteration, PSA is calculated in module
using Monte Carlo methods. The process stops for this iteration if PSA
falls outside of the target range of the bucket, but the process
continues if PSA falls within the target range of the bucket. If the
iteration continues, PSAV is calculated in module using Monte Carlo
methods. The process stops for this iteration if PSAV falls outside of
the target range of the bucket, but the process continues if PSAV falls
within the target range of the bucket. If the iteration continues, fPSA %
is calculated in module using Monte Carlo methods. The process stops for
this iteration if fPSA % falls outside of the target range of the bucket,
but the process continues if fPSA % falls within the target range of the
bucket. If the iteration continues, fPSAV % is calculated in module using
Monte Carlo methods. The process stops for this iteration if fPSAV %
falls outside of the target range of the bucket, but the process
continues if fPSAV % falls within the target range of the bucket. The
four dimensional frequency collector keeps track of the number of Monte
Carlo iterations started and the number of outcomes that fall in the 4D
bucket. Frequency is calculated by dividing the number of outcomes in the
bucket by the number of iterations started. Finally, control is passed to
the Monte Carlo iteration completion module.

[0142] FIG. 17 demonstrates an embodiment of a four dimensional frequency
generator for each year X cancer plus no cancer case. Each iteration is
initiated by the Monte Carlo iteration controller. For each iteration,
PSA is calculated using Monte Carlo methods. The process stops for this
iteration if PSA falls outside of the target range of the bucket, but the
process continues if PSA falls within the target range of the bucket. If
the iteration continues, PSAV is calculated using Monte Carlo methods.
The process stops for this iteration if PSAV falls outside of the target
range of the bucket, but the process continues if PSAV falls within the
target range of the bucket. If the iteration continues, fPSA % is
calculated using Monte Carlo methods. The process stops for this
iteration if fPSA % falls outside of the target range of the bucket, but
the process continues if fPSA % falls within the target range of the
bucket. If the iteration continues, fPSAV % is calculated using Monte
Carlo methods. The process stops for this iteration if fPSAV % falls
outside of the target range of the bucket, but the process continues if
fPSAV % falls within the target range of the bucket. The four dimensional
frequency collector keeps track of the number of Monte Carlo iterations
started and the number of outcomes that fall in the 4D bucket. Frequency
is calculated by dividing the number of outcomes in the bucket by the
number of iterations started. Finally, control is returned to the Monte
Carlo iteration completion module.

[0143] FIG. 17 shows an exemplary Monte Carlo process for generating
outcomes for year X cancer from a number of probability distributions,
where X is a measure of cancer progression. For example, X can be
measured as the number of years before or after the Transition Point,
defined as the time of progression when the cure rate begins to decline
steeply. Other reference points for measuring X may work as well. In this
example, fifteen year X cases can be considered as in Table 9.

TABLE-US-00009
TABLE 9
2 Years After the Transition Point
1 Year After the Transition Point
0 Years = At the Transition Point
1 Year Before the Transition Point
2 Years Before the Transition Point
3 Years Before the Transition Point
4 Years Before the Transition Point
5 Years Before the Transition Point
6 Years Before the Transition Point
7 Years Before the Transition Point
8 Years Before the Transition Point
9 Years Before the Transition Point
10 Years Before the Transition Point
11 Years Before the Transition Point
12 Years Before the Transition Point

Choices can increase about the functional form of the trend and the
window of time over which the trend is estimated as more test results
become available over longer periods of time. Better choices obtain more
and more valuable information from any given number of test results. An
example is presented here of a one dimensional case where only a linear
functional form is considered and the impact of a range of window sizes
is studied.

[0144] In an aspect, four-dimensional frequency distributions from the
Monte Carlo generator as described herein may be pre-computed. For the
test-result types (each of which corresponds to one of the dimensions of
the frequency distribution) that are available, the trend variation for
the dimension (as described herein can be compared directly against the
generated frequency distribution by the pre-computations. This evaluation
produces the probabilities of observing the trend evidence under the
assumption of the presence or absence of conditions such as prostate
cancer. The frequency distributions and the trend-variation distributions
can be smoothed by any number of strategies and thus captured by a single
equation or a set of several equations, or they can be captured as
frequency values in discrete buckets. The evaluation of one distribution
weighted by the other may therefore involve either continuous or discrete
variables. The multi-dimensional frequency distribution lends itself to
being pre-computed and stored because it is based largely on static
values describing the overall population and is personalized for an
individual subject by a small number of inputs which capture some
fundamental characteristics of the subject. For each discrete combination
of those inputs a frequency distribution can be stored. For a subject
whose values fall between sets of biomarker values which were used to
create stored distributions, interpolation techniques such as linear
interpolation or design of experiments may be used to extract a
personalized distribution.

[0145]FIG. 18 demonstrates an exemplary pattern of accelerating PSA
caused by progressing prostate cancer. At age 50 on the left healthy PSA
starts at 1.0 and remains constant for over a year. PSA starts to
accelerate at an increasing rate until it reaches about 12.5 at age 60 on
the right. The dotted line on FIG. 19 shows a linear trend that best fits
the example data over a ten year period from age 50 to age 60. The line
does not fit the curved data perfectly. The line underestimates PSA from
age 50 to about age 52. It overestimates PSA from about age 52 to just
over age 58. It underestimates PSA from just over age 58 until age 60. At
age 60 when the linear trend underestimates PSA by about 3.2 PSA (12.5
actual minus 9.3 for the linear trend).

[0146] The estimate of current PSA at age 60 can be improved by shortening
the window over which the linear trend is estimated. The dashed line on
FIG. 20 shows that reducing the estimation window from ten years to six
years reduces the underestimation of PSA at age 60 to about 1.4 PSA (12.5
actual minus 11.1 for the linear trend). The solid line on FIG. 20 shows
that reducing the estimation window from six years to two years further
reduces the underestimation of PSA at age 60 to less than 0.1 PSA (12.5
actual minus more than 12.4 for the linear trend).

[0147] FIG. 21 plots an example of estimated PSA at age 60 and a decline
at an accelerating rate as the estimation window size increases.

[0148] Increasing the window size can increase the number of tests
considered and the length of time over which they are considered. More
tests over a longer time can stabilize the trend and reduce the standard
deviation in the estimate of current PSA at age 60 caused by random
variation in the PSA test results. The example of FIG. 22 shows how the
standard deviation of the estimate of current PSA at age 60 declines as
window size increases.

[0149] FIG. 23 combines the results shown on FIG. 21 and FIG. 22. The
standard deviation of current PSA is plotted against the corresponding
estimate of current PSA. The results for a ten year window are shown at
the bottom left of the curve, and the results for a two year window are
shown at the top right of the curve. The steep slope near the top right
of the curve suggests that increasingly short windows provide very little
benefit in terms of an increase in estimated PSA but lead to increasing
costs in terms of steeply increasing standard deviations.

[0150] In an embodiment, a Bayesian probability of progressing cancer can
depend on both current estimates of the trends and on the confidence in
them. A higher PSA leads to a higher probability if all other variables
remain unchanged. In contrast, a higher standard deviation leads to a
lower probability if all other variables remain unchanged because there
is less confidence in the estimate of current PSA. Changing window size
may either increase or decrease the probability of progressing cancer.
For example, a reduction in widow size will increase the estimate of PSA,
which will increase the probability, but a reduction in window size will
increase the standard deviation, which will decrease the probability. The
outcome for probability depends on which of these two effects is
stronger. FIG. 24 shows a calculation of the probability of progressing
cancer as a function of window size. At the right, the window size is a
large ten years, and the probability is low because the correspondingly
low PSA estimate dominates. The probability increases as the window size
decreases from ten years to about five years, where the maximum
probability is reached. Further reductions in window size from five years
to two years cause the probability to decrease gradually as the cost of
increasing standard deviation outweighs the benefit of increasing PSA
estimates.

[0151] In these examples, a linear function for estimating the PSA trend
has been considered. These results are shown as the solid curve on FIG.
25. FIG. 20 shows how the linear function does an increasingly poor job
of matching a curved trend as the window size increases. Dynamic
screening can use higher order functions to match curved trends more
closely. An exponential function is the preferred higher order function
because on average progressing cancer accelerates in an exponential
fashion, but other higher order functions can be used. Higher order
functions, like an exponential function, provide better fits of curved
trends at the expense of higher standard deviations. Standard deviations
are higher because the increased degrees of freedom make the trend
estimates more sensitive to uncertainty in the test results. The dashed
curve on FIG. 25 shows for an exponential function a calculation of the
probability of progressing cancer as a function of window size. At the
left, the window size is a small two years, and the probability is
relatively low because the cost of a large standard deviation outweighs
the benefit of a large PSA estimate. The probability increases as the
window size increases from two years to about seven years, where the
maximum probability is reached. Increased window size increases
probability because of the benefit of decreasing standard deviation at
little cost from minimally decreasing PSA estimate. Further increases in
window size from seven years to ten years cause the probability to
decrease gradually as the cost of increasing standard deviation outweighs
the benefit of increasing PSA estimates.

[0152] Test frequency and the length of the test period help determine
which trend function produces the maximum probability of progressing
cancer. On FIG. 25 for when seven to ten years of test results are
available the maximum probability is reached using the exponential
function (dashed line) with a window size of seven years. With five to
seven years of test results the maximum probability is reached using the
exponential function (dashed curve) and the maximum window size available
(equal to the length of the test period). With less than five years of
tests results the maximum probability is reached using the linear
function (solid curve) and the maximum window size available (equal to
the length of the test period).

[0153]FIG. 26 and FIG. 27 show how variable trend functions and window
sizes can be added to the no cancer four dimensional frequency generator
and the cancer plus no cancer four dimensional frequency generator. The
trend Function and Window Size Controller determines the combination of
functions and window sizes used for each run of the Monte Carlo No Cancer
Four Dimensional Frequency Generator and No Cancer Four Dimensional
Frequency Generator, turns control over the Frequency Generator to run a
series of Monte Carlo iterations and finally keeps track of the results
returned. The trend function is used to estimate current PSA and PSAV and
the often different window sizes for PSA and PSAV. The trend function is
used to estimate current PSA, PSAV, fPSA and fPSAV needed to calculate an
estimate of current fPSA % and fPSAV % and the often different window
sizes for fPSA % and fPSAV %. The trend Function and Window Size
Controller continues to vary combinations of functions and window sizes
until enough have been run to determine the maximum probability with
reasonable accuracy.

[0154] It can take a large amount of time to run the dynamic screening
system for a sufficiently wide range of combinations of functions and
window sizes. Some time can be saved by reducing the number of iterations
for each Monte Carlo run. However, reducing iterations increases the risk
that only a small number of hits will be detected in the bucket. A small
number of hits can make the probability of progressing overly sensitive
to random hits. This sensitivity can be reduced by the 4D Frequency
Weighting in Module. Hits detected in the bucket are weighted by a
function of the 4D distance from the observed values at the center of the
bucket. Hits at the center are weighted most highly, and hits farther
away are less heavily weighted. The weighting function reduces the impact
of near misses and marginal hits and reduces the sensitivity of
progressing cancer to them. The weighting function can take the following
form:

Wfn(Δ)=the greater of O and 1-c*Δ, where cis a constant

Δ=(n ΔPSA 2+n ΔPSAV 2+n ΔfPSA % 2+n
ΔfPSAV % 2) 0.5

n ΔPSA=(PSA-tPSA)/nPSA

[0155] where tPSA is the current value
of the trend PSA

[0156] where nPSA is a normalizing PSA (possibly=tPSA)

[0156] n ΔfPSA %=(fPSA %-tPSA %)/nPSA %

[0157] where tPSA % is
the current value of the trend nfPSA %

[0158] where nfPSA % is a
normalizing fPSA % (possibly=tfPSA %)

[0158] n ΔfPSAV %=(fPSAV %-tfPSA %)/nfPSAV %

[0159] where tfPSAV
% is the current value of the trend fPSAV %

[0160] where tfPSAV % is a
normalizing fPSAV % (possibly=tfPSAV %)

[0160] n ΔfPSAV %=(fPSAV %-tfPSAV %)/nfPSAV %

[0161] where
tfPSAV % is the current value of the trend fPSAV %

[0162] where nfPSAV %
is a normalizing fPSAV % (possibly=tfPSAV %)

[0163] FIG. 28 shows an example of this triangle weighting function as a
function of PSA. The weight is 0 for PSA from 0 to 3. The weight
increases linearly from 0 at PSA 3 to 1 at PSA 6. The weight decreases
linearly from 1 at PSA 6 to 0 at PSA 9. The weight is 0 for PSA greater
than 9.

[0164]FIG. 29 shows an example of a weighting function as a function of
PSA and PSA velocity. The weight is 1 at PSA of 6 and PSAV of 0.6, shown
by the star. The weight drops to zero at the outer oval centered on the
star. Corresponding weighting functions can be constructed for thee
dimensions, four dimensions or more dimensions.

[0165] An exemplary Maximum Probability Bayes system is shown on FIG. 30.
The trend Function and Window Size Controller generates four dimensional
frequency distributions for no cancer and cancer plus no cancer cases for
an appropriate variety of trend functions and window sizes, as shown by
FIG. 26 and FIG. 27. The corresponding conditional probabilities are fed
to the Bayse Long-Term Probabilities Maximization module. The maximum
probability can be estimated and the corresponding functions and window
sizes using one or more of a variety of techniques that can include
design of experiments, response surface methods, analytic optimization,
hill climbing optimization and any other methods that may be effective.

[0166] For example, FIG. 31 shows how the results that can be observed for
both PSA only using analytic optimization (the curve maximum for each
curve) using one dimensional response surfaces (the curves). Maximum
probability is reached using the linear function (solid curve) and
maximum window size for test periods from two to five years. It is
reached using the exponential function (dashed curve) for test periods
greater than five years with maximum window size for test periods from
five to seven years and a seven year window size for longer test periods.

[0167]FIG. 32 shows how the results that can be observed for both PSA and
PSA velocity. The window size for PSA is shown on the horizontal axis.
The window size for PSAV is shown on the vertical axis. Probability is
perpendicular to the page and represented as a contour map. The star
shows the location of the maximum probability. The two circles around the
star show contours of lower probability with the lowest probability shown
by the largest circle. These contour maps can be constructed using two
dimensional quadratic response surfaces, and the maximums can be found
using analytic maximization. In this case, and in many cases, maximum
probability is reached using different window sizes for different
variables. Moreover, the window sizes that produce the maximum
probability are likely to vary as a result of variation in the number of
tests, total testing period, the amount of cancer progression and the
curvature of the PSA trend, the functional form chosen, and a variety of
other variables and circumstances.

[0168]FIG. 33 shows an exemplary concept of maximum probability and
window sizes in a 3D situation and demonstrates its function in 4D or
higher situations.

[0169] Embodiments of the present invention extend the capabilities of
dynamic screening. The capabilities relate to multiple benign conditions
as well as progressing cancer. Included are descriptions covering
temporary benign conditions, long-term conditions, both benign and
cancer, and tuning distributions using long-term conditions as the
example.

[0170] PSA and free PSA tests can have results that are greater than or
smaller than their predicted trend values. Dynamic screening may label
them anomalous and excludes them from subsequent trend estimation if
their deviations, including the ratio free PSA to PSA, exceed certain
tolerance ranges. Anomalous results with PSA values substantially below
the trend are rare and may be caused by a variety of situations,
including test error or test recording error. Anomalous results with PSA
values substantially above the trend are more frequent and may be caused
by one or more benign conditions. Dynamic screening estimates the
probability of these benign conditions using Bayesian processes.

Plurality of Medical Conditions

[0171] In an embodiment, capabilities are added to dynamic screening to
allow the calculation of the probability of benign prostate conditions.
Two exemplary conditions include, but are not limited to, inflammation
prostatitis and infection prostatitis along with a category for other
temporary conditions. FIG. 34 shows the probability of each of these two
temporary benign conditions for anomalous test results over time for a
man. These probabilities can be used to inform decisions about imaging,
testing and treatment of possible conditions. Anomalous test results with
PSA values below the trend are indicated by gray bars below the
horizontal axis.

[0172] Three similar Bayes processes can be used to calculate the
probability of the prostate conditions: inflammation prostatitis,
infection prostatitis and other temporary conditions. The process for
calculating the probability of progressing cancer has been disclosed
previously. In an embodiment, the Bayes process uses three elements: the
prior probability of the condition, the probability of the observed trend
values and the incremental change from them conditional on all conditions
and the probability of the observed trend values and the incremental
change from them conditional on the absence of the condition but with all
other conditions possible. Prior probabilities may be a function of age,
race, genetics, demographics, past experience with the conditions and
other considerations as shown in FIG. 35.

[0173] Temporary conditions of the prostate are partitioned into 7
different condition combinations that are composed of three different
prostate conditions as shown in FIG. 36. This partition allows the use of
an extension of Bayes theorem for a partition of the event space--all
relevant long-term prostate conditions in this case.

[0174] Definitions

[0175] Xi=Any one of several prostate conditions,
such as O, I, I.

[0176] Xj=One specific prostate condition, such as O or
I or I.

[0177] Yi=Any one of several condition partitions, such as O, OI,
OII.

[0185] The probability generator for all temporary prostate conditions
consolidates output from three separate probability generators:
inflammation prostatitis, infection prostatitis and other temporary
conditions as shown in FIG. 37 and FIG. 38. Total values are stored from
iterations of the Monte Carlo process for four variables: PSA and PSA
increment from the trend, and free PSA and free PSA increment from the
trend. Ratios are calculated for free PSA % (=free PSA/PSA) and free PSA
Incrment % (=free PSA increment/PSA increment). Other probability
generators are similar with one module removed. For example, the other
condition generator starts with the all temporary prostate conditions
generator and removes the other conditions generator.

[0186] The probability distributions of each prostate condition can be
affected by past medical experience with the conditions, and the results
of imaging, tests, treatment and other medical procedures as shown in
FIG. 39. For example, prostatic secretions can be cultured for bacterial
infections. The results can affect the probability distributions produced
by the infection prostatitis module. In a similar way, treatment with
antibiotics can affect PSA levels. The outcome can affect the
distributions produced by the infection prostatitis module. For example,
a negative bacterial culture and no impact from antibiotic treatment may
reduce the probability of infection prostatitis and increase the
probability of inflammation and the probability of other conditions. In
contrast, a positive bacterial culture and/or beneficial impact of
antibiotic treatment may increase the probability of infection
prostatitis to a high level and reduce the probability of inflammation
and the probability of other conditions. Examples of this are shown in as
shown in FIG. 40.

[0187] In an embodiment, other clinical conditions PSA increment is the
product of the other conditions leak rate increment, drawn from the other
conditions LI % distribution, and trend PSA from the PSA module as shown
in FIG. 41. Temporary PSA is the sum of trend PSA from the PSA module and
PSA increment. In other conditions, free PSA increment is the product of
the other conditions free PSA %, drawn from the other conditions fPSA %
distribution (which may be influenced by the healthy and BPH fPSA % s),
and calculated PSA increment. Temporary free PSA is the sum of trend free
PSA from the free PSA module and free PSA increment.

[0188] In an embodiment, inflammation PSA increment is the product of the
inflammation leak rate increment, drawn from the inflammation LI %
distribution, and trend PSA from the PSA module as shown in FIG. 41.
Temporary PSA is the sum of trend PSA from the PSA module and PSA
increment. Inflammation free PSA increment is the product of the
inflammation free PSA %, drawn from the inflammation fPSA % distribution
(which may be influenced by the healthy and BPH fPSA % s), and calculated
PSA increment. Temporary free PSA is the sum of trend free PSA from the
free PSA module and free PSA increment.

[0189] In another embodiment, infection PSA increment is the product of
the infection leak rate increment, drawn from the infection LI %
distribution, and trend PSA from the PSA module as shown in FIG. 41.
Temporary PSA is the sum of trend PSA from the PSA module and PSA
increment. Infection free PSA increment is the product of the infection
free PSA %, drawn from the infection fPSA % distribution (which may be
influenced by the healthy and BPH fPSA % s), and calculated PSA
increment. Temporary free PSA is the sum of trend free PSA from the free
PSA module and free PSA increment.

[0190] A total and calculation module can consolidate output from the
separate probability generators for the three temporary prostate
conditions: other temporary conditions, inflammation prostatitis, and
infection prostatitis. Values are totaled for four variables: PSA and PSA
increment, and free PSA and free PSA increment. Ratios are calculated for
free PSA % (=free PSA/PSA) and free PSA increment % (=free PSA
increment/PSA increment).

[0191] The graph in FIG. 42 shows an example of how the probability of the
presence of infection (P %) for a man tends to increase with age and past
history of infection. The more a man has had past infections the more
likely he is to have one now.

[0192] The probability density for an infection leak increment percent (LI
%) can depend on past experience as shown in FIG. 43. A man with no
history of infections will have a declining population based
distribution, shown in light gray. However, one or many infections with
high LI % s will shift the distributions to higher peaks at larger LI %
s, as shown by the dark gray and black distributions.

[0193] The probability density for free PSA % (fPSA %) can also depend on
past experience as shown in FIG. 44. A man with no history of infections
will have a low and broad population based distribution, shown in light
gray. However, one or many infections with very low fPSA % s will shift
the distributions to higher peaks at smaller fPSA % s, as shown by the
dark gray and black distributions.

[0194] In an aspect, an elevated or increasing PSA trend is an indication
that a long-term condition may be affecting the prostate. Dynamic
screening can estimate the probability of these conditions using Bayesian
processes.

[0195] In an embodiment, probability of other long-term prostate
conditions, in addition to progressing cancer, can be calculated. For
example, long-term conditions considered include, but are not limited to,
volume growth due to BPH, inflammation prostatitis and infection
prostatitis. The exemplary graph in FIG. 45 shows how the probability of
each of these three benign conditions can change over time for a man.
These probabilities can be used to inform decisions about imaging,
testing and treatment of possible conditions.

[0196] In another embodiment, four similar Bayes processes are used to
calculate the probability of the prostate conditions: volume growth due
to BPH, inflammation prostatitis, infection prostatitis and progressing
cancer as shown in FIG. 46. The process for calculating the probability
of progressing cancer has been disclosed previously. The Bayes process
uses three elements: the prior probability of the condition, the
probability of the observed trend values conditional on all conditions
and the probability of the observed trend values conditional on the
absence of the condition but with all other conditions possible. Prior
probabilities may be a function of age, race, genetics, demographics and
other considerations.

[0197] The status of the prostate is partitioned into 16 different
condition combinations that are composed of five different prostate
conditions as shown in FIG. 47. The condition combinations are mutually
exclusive and collectively exhaustive. This partition allows the use of
an extension of Bayes theorem for a partition of the event space--all
relevant long-term prostate conditions in this case.

[0198] Definitions

[0199] Xi=Any one of several medical conditions, such
as H, V, C.

[0200] Xj=One specific condition, such as H or V or C.

[0201]
Yi=Any one of several condition partitions, such as H, HV, HVI/C.

[0209] In an aspect of the invention, a probability generator for all
prostate conditions consolidates output from five exemplary separate
probability generators for a healthy prostate and the four prostate that
include without limitation: volume growth due to BPH, inflammation
prostatitis, infection prostatitis and progressing cancer as shown in
FIG. 48, FIG. 49, and FIG. 50. Total values are stored from iterations of
the Monte Carlo process for six exemplary variables that include without
limitation: prostate volume and volume velocity, PSA and PSA velocity,
and free PSA and free PSA velocity. Ratios can be calculated for free PSA
% (=free PSA/PSA) and free PSA velocity % (=free PSA velocity/PSA
velocity). In an embodiment, other probability generators can be similar
with one module removed. For example, the no BPH volume growth generator
starts with the all prostate conditions generator and removes the BPH
volume growth generator.

[0210] The probability distributions of each prostate condition can be
affected by past experience and the results of imaging, tests, treatment
and other medical procedures as shown in FIG. 51. For example, the
prostate can be imaged using ultrasound or MRI equipment and its volume
can be measured from the images. This measurement constrains the
distributions of prostate volume, PSA and free PSA. For example,
prostatic secretions can be cultured for bacterial infections. The
results will affect the probability distributions produced by the
infection prostatitis module. In a similar way, treatment with
antibiotics can affect PSA levels. The outcome can affect the
distributions produced by the infection prostatitis module. For example,
a negative bacterial culture and no impact from antibiotic treatment will
reduce the probability of infection prostatitis and increase the
probability of other conditions, including progressing cancer. In
contrast, a positive bacterial culture and/or beneficial impact of
antibiotic treatment will increase the probability of infection
prostatitis to a high level and reduce the probability of other
conditions, including progressing cancer.

[0211] The flow charts in FIG. 52 show an embodiment of the no cancer
probability generators. The small gray boxes show the probability
distributions from which draws are made during each Monte Carlo
iteration. In an embodiment, healthy prostate volume can be drawn from
the Vol distribution. PSA is the product of PSA density, drawn from the
healthy PSAD distribution, and the healthy prostate volume draw. Free PSA
is the product of the free PSA %, drawn from the fPSA % distribution, and
calculated PSA. In another embodiment, BPH volume is drawn from the Vol
distribution, which may be influenced by the healthy Vol distribution as
shown by the dotted line. BPH volume velocity is drawn from the VolVel
distribution, which may be influenced by the healthy Vol distribution and
the BPH Vol distribution, as shown by the dotted lines. PSA is the
product of PSA density, drawn from the BPH PSAD distribution (which may
be influenced by the healthy PSAD distribution as shown by the A
connector), and the BPH prostate volume draw. PSA velocity is the product
of PSA density, drawn from the BPH PSAD distribution (which may be
influenced by the healthy PSAD distribution as shown by the A connector),
and the BPH prostate volume velocity draw. Free PSA is the product of the
free PSA %, drawn from the BPH fPSA % distribution (which may be
influenced by the healthy fPSA % distribution as shown by the B
connector), and calculated PSA. Free PSA velocity is the product of the
free PSA %, drawn from the BPH fPSA % distribution (which may be
influenced by the healthy fPSA % distribution as shown by the B
connector), and calculated PSA velocity. A summation module can add
healthy prostate and BPH volume growth variables: volume, volume
velocity, PSA, PSA velocity, free PSA and free PSA velocity.

[0212] In an embodiment, inflammation prostatitis PSA is the product of
the inflammation leak rate, drawn from the inflammation L % distribution,
and Sum PSA from the summation module. PSA velocity has two sources.
First, PSA velocity is the product of the leak rate velocity, drawn from
the LV % distribution, which may be influenced by L %, and calculated
inflammation PSA. Second, PSA velocity is the product of the leak rate,
drawn from the inflammation L % distribution, and Sum PSA velocity from
the summation module. Both sources of PSA velocity are summed in the
module. Inflammation prostatitis free PSA can be the product of
inflammation free PSA %, drawn from the inflammation fPSA % distribution
(which may be influenced by the healthy and BPH fPSA % s), and calculated
inflammation PSA. Free PSA velocity has two sources. First, free PSA
velocity is the product of the free PSA %, drawn from the fPSA %
distribution, and calculated inflammation PSA velocity (which came from
the leak rate velocity %. Second, free PSA velocity is the product of the
free PSA %, drawn from the inflammation fPSA % distribution, and
calculated inflammation PSA velocity (which came from PSAV caused by
volume velocity). Both sources of free PSA velocity are summed in the
module.

[0213] In an embodiment, infection prostatitis PSA is the product of the
infection leak rate, drawn from the infection L % distribution, and Sum
PSA from the summation module as shown in FIG. 56. PSA velocity has two
sources. First, PSA velocity is the product of the leak rate velocity,
drawn from the LV % distribution, which may be influenced by L %, and
calculated infection PSA. Second, PSA velocity is the product of the leak
rate, drawn from the infection L % distribution, and Sum PSA velocity
from the summation module. Both sources of PSA velocity are summed in the
module. Infection prostatitis free PSA can be the product of infection
free PSA %, drawn from the infection fPSA % distribution (which may be
influenced by the healthy and BPH fPSA % s), and calculated infection
PSA. Free PSA velocity has two sources. First, free PSA velocity is the
product of the free PSA %, drawn from the fPSA % distribution, and
calculated infection PSA velocity (which came from the leak rate velocity
%). Second, free PSA velocity is the product of the free PSA %, drawn
from the infection fPSA % distribution, and calculated infection PSA
velocity (which came from PSAV caused by volume velocity). Both sources
of free PSA velocity are summed in the module.

[0214] In an example, a total and calculation module consolidates output
from the separate probability generators for the four benign prostate
conditions: healthy prostate, volume growth due to BPH, inflammation
prostatitis, infection prostatitis and progressing cancer. Values are
totaled for six variables: prostate volume and volume velocity, PSA and
PSA velocity, and free PSA and free PSA velocity. Ratios are calculated
for free PSA % (=free PSA/PSA) and free PSA velocity % (=free PSA
velocity/PSA velocity).

[0215] In an embodiment, a healthy prostate module has three distributions
for Monte Carlo draws: Vol, PSAD and fPSA % as shown in FIG. 53. Vol is
the healthy volume distribution from which a man's volume is drawn in
each Monte Carlo iteration. It is affected by age, demographics and past
volume measurements. An example for Vol is shown with a mean of 28.0 ccs
and standard deviation of 4.3 ccs. PSAD is the healthy PSA density
distribution from which a man's PSA density is drawn in each Monte Carlo
iteration. It is affected by age, demographics and past PSA trends and
volume measurements. An example for PSAD is shown with a mean of 0.035
PSA/cc and standard deviation of 0.008. fPSA % is the healthy free PSA %
distribution from which a man's fPSA % is drawn in each Monte Carlo
iteration. It is affected by age, demographics and past free PSA and PSA
trends. An example for fPSA % is shown with a mean of 28% and standard
deviation of 7%.

[0216] In an embodiment, the BPH volume growth module has four
distributions for Monte Carlo draws: Vol, VolVel, PSAD and fPSA % as
shown in FIG. 54. Vol is the BPH volume multiplier distribution from
which a man's volume increase ratio to his healthy volume is drawn in
each Monte Carlo iteration. Vol has two parts: the presence probability,
P %, and the distribution of its values. P % is the binary probability
that BPH is present and has caused an increase in the PSA trend. It is
affected by age, demographics, the healthy volume draw and past volume
measurements. P % is set equal to 100% for the certain module in the bold
box. The distribution density function is likely to decline in density
with increasing Vol. It may change based on past experience. VolVel is
the BPH volume velocity distribution from which a man's volume velocity
is drawn in each Monte Carlo iteration. VolVel is the annual rate of
increase in prostate volume due to BPH. VolVel has two parts: the
presence probability, P %, and the distribution of its values. P % is
either 1 or 0 based on the P % draw in the Vol module. P % is set equal
to 100% for the certain module in the bold box. The distribution density
function is likely to decline in density with increasing VolVel. It may
change based on age, demographics, drawn values for healthy and BPH
volumes, and volume measurements. PSAD is the BPH volume PSA density
distribution from which a man's BPH PSA density is drawn in each Monte
Carlo iteration. It is affected by age, demographics, drawn healthy PSAD
and past PSA trends and volume measurements. An example for PSAD is shown
with a mean equal to the BPH % times the healthy PSAD draw and standard
deviation of CV % times the mean. BPH % tends to roughly 100% because BPH
density is similar to healthy density. fPSA % is the BPH volume growth
free PSA % distribution from which a man's fPSA % is drawn in each Monte
Carlo iteration. It is affected by age, demographics and past free PSA
and PSA trends. An example for fPSA % is shown. It has a mean of BPH %
times the drawn healthy fPSA %. Typically, BPH % is greater than 100%
because BPH volume growth tends to increase free PSA %. CV % may be
relatively small.

[0217] In an embodiment, the inflammation prostatitis module has three
distributions for Monte Carlo draws: L %, LV % and fPSA % as shown in
FIG. 55. L % is the leak rate percent and has two parts: the presence
probability, P %, and the distribution of its values. P % is the binary
probability that inflammation is present and has caused an increase in
the PSA trend. It is based on age, demographics and past experience with
inflammation. P % is set equal to 100% for the certain module in the bold
box. The distribution density function is likely to decline in density
with increasing L %. It may change based on past experience. LV % is the
leak rate velocity percent and describes how increasing inflammation
increases the amount of PSA over time by leaking higher percentages of
PSA. LV % has two parts: the presence probability, P %, and the
distribution of its values. P % is either 1 or 0 based on the P % draw in
the L % module. P % is set equal to 100% for the certain module in the
bold box. The distribution density function is likely to decline in
density with increasing LV %. It may change based on past experience. An
example for fPSA % is shown with the mean equal to Inflammation % times
the ratio of healthy plus BPH free PSA to PSA. Typically, Inflammation %
is roughly 100% because inflammation tends to increase the amount of PSA
leakage without changing the free PSA % substantially--resulting in a
relatively small CV %.

[0218] In an embodiment, the infection prostatitis module has three
distributions for Monte Carlo draws: L %, LV % and fPSA % as shown in
FIG. 56. L % is the leak rate percent and has two parts: the presence
probability, P %, and the distribution of its values. P % is the binary
probability that infection is present and has caused an increase in the
PSA trend. It is based on age, demographics and past experience with
infection. P % is set equal to 100% for the certain module in the bold
box. The distribution density function is likely to decline in density
with increasing L %. It may change based on past experience. LV % is the
leak rate velocity percent and describes how increasing infection
increases the amount of PSA over time by leaking higher percentages of
PSA. LV % has two parts: the presence probability, P %, and the
distribution of its values. P % is either 1 or 0 based on the P % draw in
the L % module. P % is set equal to 100% for the certain module in the
bold box. The distribution density function is likely to decline in
density with increasing LV %. It may change based on past experience. An
example for fPSA % is shown with the mean equal to Infection % times the
ratio of healthy plus BPH free PSA to PSA. Typically, Infection % is much
less than 100% because infection tends to decrease the amount of PSA
leakage while decreasing free PSA % substantially. CV % may be relatively
large.

Model Tuning

[0219] An enormous amount of data can be needed to define all the
underlying distributions completely. In practice, the amount of data
needed to define the distributions is not practical to obtain. Therefore,
an iterative process is needed to tune the parameters of the underlying
distributions so that known relationships are satisfied and the overall
distributions conform to population studies.

[0220] In an aspect, an iterative Monte Carlo process generates
multi-dimensional distributions for men of a given age without prostate
cancer. Static parts of the distribution (no velocities as shown below)
can be validated against available distributions. For example, the Center
for Disease Control has published distributions of PSA, free PSA and free
PSA % for U.S. men in ten year age ranges from age forty to age eighty
and above, and the Mayo Clinic has published prostate volume and PSA
distributions for men from age forty to age eighty in Olmsted County, MN.
Distributions like these constrain the overall distributions generated by
the Monte Carlo process. Details of these distributions and other medical
studies constrain the results of the specific probability generators and
the relationships among them. For example, the CDC distributions show a
significant decline in free PSA % for higher levels of PSA. This result
strongly suggests that infection prostatitis accounts for an increasing
proportion of higher PSA results because it is the only benign condition
that produces free PSA in a percent that is significantly lower than the
other benign conditions. Exemplary tuning of parameters and validation of
detailed distributions is demonstrated in as shown in FIG. 58.

[0221] In an embodiment, the first step of a tuning process of the
invention is to tune the no cancer static distribution for a given age
(t=0), such as age 55. No velocities need be calculated for this static
distribution as shown in FIG. 57. Unused modules are shown as blank in
the figure. Starting parameters for all underlying distributions, the
gray boxes, are chosen consistent with known relationships, and an
iterative Monte Carlo process is run. The resulting multi-dimensional
distribution is compared to the population distribution. New parameters
for all underlying distributions are chosen consistent with known
relationships, and an iterative Monte Carlo process is run again. The
resulting multi-dimensional distribution is compared to the population
distribution. Over many cycles through this process the multi-dimensional
distribution converges on the population distribution while maintaining
known relationships to the extent possible. Advanced solution algorithms
may be used to speed the convergence process. This tuning process is
repeated for a range of ages, such as age 45, 55, 65 and 75.

[0222] In another embodiment of a tuning process of the invention, the
next step is to tune the velocity distribution parameters. Static results
for a given year plus the changes caused by velocities accumulated over a
ten year period should yield the static distribution ten years later. An
iterative Monte Carlo process using static and velocity parameters
generates multi-dimensional distributions for men ten years later without
prostate cancer. Static parts of the distribution can be validated
against available distributions. For example, the Center for Disease
Control has published distributions of PSA, free PSA and free PSA % for
U.S. men in ten year age ranges from age forty to age eighty and above,
and the Mayo Clinic has published prostate volume and PSA distributions
for men from age forty to age eighty in Olmsted County, Minn.
Distributions like these constrain the overall distributions generated by
the Monte Carlo process. Details of these distributions and other medical
studies constrain the results of the specific probability generators and
the relationships among them.

[0223] In another embodiment, a tuning process involves using the
parameters for the no cancer static distribution for a given age (t=0),
such as age 55. A second step is to tune velocity parameters to achieve
the no cancer static distribution for ten years later (t=10), such as age
65 as shown in FIG. 59 and FIG. 60. Starting parameters for all
underlying velocity distributions are chosen consistent with known
relationships, and an iterative Monte Carlo process is run. The resulting
multi-dimensional static distribution for ten years later is compared to
the population distribution. New parameters for all underlying velocity
distributions are chosen consistent with known relationships, and an
iterative Monte Carlo process is run again. The resulting
multi-dimensional static distribution is compared to the population
distribution. Over many cycles through this process the multi-dimensional
static distribution converges on the population distribution while
maintaining known relationships to the extent possible. Advanced solution
algorithms may be used to speed the convergence process. This tuning
process is repeated for a range of ages.

Integrated Health Systems

[0224] In another aspect, a medical information system for assessing a
disease of a subject is provided that comprises: an input device for
receiving subject data; a processor that assesses a probability of said
data relating to historical data; a storage unit in communication with
the processor having a database for: (i) storing the subject data; (ii)
storing historical data related to the disease; and an output device that
transmits information relating to the probability of said data relating
to historical data to an end user.

[0225] Also provided herein is a method for assessing a disease in a
subject comprising: collecting data from the subject corresponding to a
biomarker for the disease at at least two different times, wherein the
data corresponding to the at least two different times form a trend;
exporting said data for manipulation of said data by executing a method
herein; and importing the results of said manipulation to an end user.
For example, data is collected at a first location, such as a hospital,
the data is exported to a second location, such as a remote server in any
remote location, where a method of the invention is executed to obtain
information regarding the disease in a subject, and then the information
is imported from the remote location back to the first location, such as
the point-of-care in the hospital, or the information is imported to a
third location, such as a database.

[0226] It is further noted that the systems and methods may be implemented
on various types of computer architectures, such as for example on a
networked system or in a client-server configuration, or in an
application service provider configuration, on a single general purpose
computer or workstation. The systems and methods may include data signals
conveyed via networks (for example, local area network, wide area
network, internet, combinations thereof), fiber optic medium, carrier
waves, and wireless networks for communication with one or more data
processing devices. The data signals can carry any or all of the data
disclosed herein (for example, user input data, the results of the
analysis to a user) that is provided to or from a device.

[0227] Additionally, the methods and systems described herein may be
implemented on many different types of processing devices by program code
comprising program instructions that are executable by the device
processing subsystem. The software program instructions may include
source code, object code, machine code, or any other stored data that is
operable to cause a processing system to perform methods described
herein.

[0228] The systems' and methods' data (for example, associations,
mappings) may be stored and implemented in one or more different types of
computer-implemented ways, such as different types of storage devices and
programming constructs (for example, data stores, RAM, ROM, Flash memory,
flat files, databases, programming data structures, programming
variables, IF-THEN (or similar type) statement constructs). It is noted
that data structures describe formats for use in organizing and storing
data in databases, programs, memory, or other computer-readable media for
use by a computer program.

[0229] The systems and methods may be provided on many different types of
computer-readable media including computer storage mechanisms (for
example, CD-ROM, diskette, RAM, flash memory, computer's hard drive,
magnetic tape, and holographic storage) that contain instructions (for
example, software) for use in execution by a processor to perform the
methods' operations and implement the systems described herein.

[0230] The computer components, software modules, functions, data stores
and data structures described herein may be connected directly or
indirectly to each other in order to allow the flow of data needed for
their operations. It is also noted that the meaning of the term module
includes but is not limited to a unit of code that performs a software
operation, and can be implemented for example as a subroutine unit of
code, or as a software function unit of code, or as an object (as in an
object-oriented paradigm), or as an applet, or in a computer script
language, or as another type of computer code. The software components
and/or functionality may be located on a single computer or distributed
across multiple computers depending upon the situation at hand.

[0231] In yet another aspect, a computer readable medium is provided
including computer readable instructions, wherein the computer readable
instructions instruct a processor to execute step a) of the methods
described above. The instructions can operate in a software runtime
environment.

[0232] In yet another aspect, a data signal is provided that can be
transmitted using a network, wherein the data signal includes said
posterior probability calculated in a step of the methods described
above. The data signal can further include packetized data that is
transmitted through wired or wireless networks.

[0233] In an aspect, a computer readable medium comprises computer
readable instructions, wherein the instructions when executed carry out a
calculation of the probability of a medical condition in a patient based
upon data obtained from the patient corresponding to at least one
biomarker. The computer readable instructions can operate in a software
runtime environment of the processor. In an embodiment, a software
runtime environment provides commonly used functions and facilities
required by the software package. Examples of a software runtime
environment include, but are not limited to, computer operating systems,
virtual machines or distributed operating systems. As will be appreciated
by those of ordinary skill in the art, several other examples of runtime
environment exist. The computer readable instructions can be packaged and
marketed as a software product or part of a software package. For
example, the instructions can be packaged with an assay kit for PSA.

[0234] The computer readable medium may be a storage unit. It is
appreciated by those skilled in the art that computer readable medium can
also be any available media that can be accessed by a server, a
processor, or a computer. The computer readable medium can be
incorporated as part of the computer-based system, and can be employed
for a computer-based assessment of a medical condition.

[0235] In an embodiment, the calculation of a probability can be carried
out on a computer system. The computer system can comprise any or all of
the following: a processor, a storage unit, software, firmware, a network
communication device, a display, a data input, and a data output. A
computer system can be a server. A server can be a central server that
communicates over a network to a plurality of input devices and/or a
plurality of output devices. A server can comprise at least one storage
unit, such as a hard drive or any other device for storing information to
be accessed by a processor or external device, wherein the storage unit
can comprise one or more databases. In an embodiment, a database can
store hundreds to millions of data points corresponding to a biomarker
from hundreds to millions of subjects. A storage unit can also store
historical data read from an external database or as input by a user. In
an embodiment, a storage unit stores data received from an input device
that is communicating or has communicated with the server. A storage unit
can comprise a plurality of databases. In an embodiment, each of a
plurality of databases corresponds to each of a plurality of biomarkers.
In another embodiment, each of a plurality of databases corresponds to
each of a plurality of possible medical conditions of a subject. An
individual database can also comprise information for a plurality of
possible medical conditions or a plurality of biomarkers or both.
Further, a computer system can comprise multiple servers.

[0236] A processor can access data from a storage unit or from an input
device to perform a calculation of an output from the data. A processor
can execute software or computer readable instructions as provided by a
user, or provided by the computer system or server. The processor may
have a means for receiving patient data directly from an input device, a
means of storing the subject data in a storage unit, and a means for
processing data. The processor may also include a means for receiving
instructions from a user or a user interface. The processor may have
memory, such as random access memory, as is well known in the art. In one
embodiment, an output that is in communication with the processor is
provided.

[0237] After performing a calculation, a processor can provide the output,
such as from a calculation, back to, for example, the input device or
storage unit, to another storage unit of the same or different computer
system, or to an output device. Output from the processor can be
displayed by data display. A data display can be a display screen (for
example, a monitor or a screen on a digital device), a print-out, a data
signal (for example, a packet), an alarm (for example, a flashing light
or a sound), a graphical user interface (for example, a webpage), or a
combination of any of the above. In an embodiment, an output is
transmitted over a network (for example, a wireless network) to an output
device. The output device can be used by a user to receive the output
from the data-processing computer system. After an output has been
received by a user, the user can determine a course of action, or can
carry out a course of action, such as a medical treatment when the user
is medical personnel. In an embodiment, an output device is the same
device as the input device. Example output devices include, but are not
limited to, a telephone, a wireless telephone, a mobile phone, a PDA, a
flash memory drive, a light source, a sound generator, a fax machine, a
computer, a computer monitor, a printer, an iPOD, and a webpage. The user
station may be in communication with a printer or a display monitor to
output the information processed by the server.

[0238] A client-server, relational database architecture can be used in
embodiments of the invention. A client server architecture is a network
architecture in which each computer or process on the network is either a
client or a server. Server computers are typically powerful computers
dedicated to managing disk drives (file servers), printers (print
servers), or network traffic (network servers). Client computers include
PCs (personal computers) or workstations on which users run applications,
as well as example output devices as disclosed herein. Client computers
rely on server computers for resources, such as files, devices, and even
processing power. In some embodiments of the invention, the server
computer handles all of the database functionality. The client computer
can have software that handles all the front-end data management and can
also receive data input from users.

[0239] A database can be developed for a medical condition in which
relevant information is filtered or obtained over a communication network
(for example, the internet) from one or more data sources, such as a
public remote database, an internal remote database, and a local
database. A public database can include online sources of free data for
use by the general public, such as, for example, databases supplied by
the U.S. Department of Health and Human Services. For example, an
internal database can be a private internal database belonging to
particular hospital, or a SMS (Shared Medical system) for providing data.
A local database can comprise, for example, biomarker data relating to a
medical condition. The local database may include data from a clinical
trial. It may also include data such as blood test results, patient
survey responses, or other items from patients in a hospital.

[0240] Subject data can be stored with a unique identifier for recognition
by a processor or a user. In another step, the processor or user can
conduct a search of stored data by selecting at least one criterion for
particular patient data. The particular patient data can then be
retrieved.

[0241] In an example, a subject or medical professional enters medical
data from a biomarker assay into a webpage. The webpage transmits the
data to a computer system or server, wherein the data is stored and
processed. For example, the data can be stored in databases the computer
systems. Processors in the computer systems can perform calculations
comparing the input data to historical data from databases available to
the computer systems. The computer systems can then store the output from
the calculations in a database and/or communicate the output over a
network to an output device, such as a webpage or email. After a user has
received an output from the computer system, the user can take a course
of medical action according to the output. For example, if the user is a
physician and the output is a probability of cancer above a threshold
value, the physician can then perform or order a biopsy of the suspected
tissue.

[0242]FIG. 61 demonstrates an example computer system of the invention. A
set of users can use a web browser to enter data from a biomarker assay
into a graphical user interface of a webpage. The webpage is a graphical
user interface associated with a front end server, wherein the front end
server can communicate with the user's input device (for example, a
computer) and a back end server. The front end server can either comprise
or be in communication with a storage device that has a front-end
database capable of storing any type of data, for example user account
information, user input, and reports to be output to a user. Data from
each user (for example, biomarker values and subject profiles) can be
then be sent to a back end server capable of manipulating the data to
generate a result. For example, the back end server can calculate a
probability that a subject has a medical condition using the data input
by the user. A back end server can comprise historical data relating to a
medical condition to be evaluated, or a plurality of medical conditions.
The back end server can then send the result of the manipulation or
calculation back to the front end server where it can be stored in a
database or can be used to generate a report. The results can be
transmitted from the front end server to an output device (for example, a
computer with a web browser) to be delivered to a user. A different user
can input the data and receive the data. In an embodiment, results are
delivered in a report. In another embodiment, results are delivered
directly to an output device that can alert a user.

[0243] In an embodiment, a method of the invention comprises obtaining a
sample from a subject, wherein the sample contains a biomarker. The
sample can be obtained by the subject or by a medical professional.
Examples of medical professionals include, but are not limited to,
physicians, emergency medical technicians, nurses, first responders,
psychologists, medical physics personnel, nurse practitioners, surgeons,
dentists, and any other obvious medical professional as would be known to
one skilled in the art. The sample can be obtained from any bodily fluid,
for example, amniotic fluid, aqueous humor, bile, lymph, breast milk,
interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper's fluid
(pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus,
saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen,
sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid,
intracellular fluid, and vitreous humour. In an example, the sample is
obtained by a blood draw, where the medical professional draws blood from
a subject, such as by a syringe. The bodily fluid can then be tested to
determine the prevalence of the biomarker. Biological markers, also
referred to herein as biomarkers, according to the present invention
include without limitation drugs, prodrugs, pharmaceutical agents, drug
metabolites, biomarkers such as expressed proteins and cell markers,
antibodies, serum proteins, cholesterol, polysaccharides, nucleic acids,
biological analytes, biomarker, gene, protein, or hormone, or any
combination thereof. At a molecular level, the biomarkers can be
polypeptide, glycoprotein, polysaccharide, lipid, nucleic acid, and a
combination thereof.

[0244] Example biomarker assays include, but are not limited to, DNA
assays, including DNA microarrays, Southern blots, Northern blots,
ELISAs, flow cytometry, Western blots, PSA assays, and immunoassays. The
information from the assay can be quantitative and sent to a computer
system of the invention. The information can also be qualitative, such as
observing patterns or fluorescence, which can be translated into a
quantitative measure by a user or automatically by a reader or computer
system. In an embodiment, the subject can also provide information other
than biomarker assay information to a computer system, such as race,
height, weight, age, gender, eye color, hair color, family medical
history and any other information that may be useful to the user, as
would be obvious.

[0245] Information can be sent to a computer system automatically by a
device that reads or provides the data from a biomarker assay. In another
embodiment, information is entered by a user (for example, the subject or
medical professional) into a computer system using an input device. The
input device can be a personal computer, a mobile phone or other wireless
device, or can be the graphical user interface of a webpage. For example,
a webpage programmed in JAVA can comprise different input boxes to which
text can be added by a user, wherein the string input by the user is then
sent to a computer system for processing. The subject may input data in a
variety of ways, or using a variety of devices. Data may be automatically
obtained and input into a computer from another computer or data entry
system. Another method of inputting data to a database is using an input
device such as a keyboard, touch screen, trackball, or a mouse for
directly entering data into a database.

[0246] In another embodiment, a system can further include a medical test
for testing said subject for said medical condition. The medical test can
be a PSA assay. In yet another embodiment, a system can further include a
medical treatment for treating said subject for said medical condition.
The medical treatment can be selected from a group including the
following: a pharmaceutical, surgery, organ resection, and radiation
therapy.

[0247] In an embodiment, a computer system comprises a storage unit, a
processor, and a network communication unit. For example, the computer
system can be a personal computer, laptop computer, or a plurality of
computers. The computer system can also be a server or a plurality of
servers. Computer readable instructions, such as software or firmware,
can be stored on a storage unit of the computer system. A storage unit
can also comprise at least one database for storing and organizing
information received and generated by the computer system. In an
embodiment, a database comprises historical data, wherein the historical
data can be automatically populated from another database or entered by a
user.

[0248] In an embodiment, a processor of the computer system accesses at
least one of the databases or receives information directly from an input
device as a source of information to be processed. The processor can
perform a calculation on the information source, for example, performing
dynamic screening or a probability calculation method. After the
calculation the processor can transmit the results to a database or
directly to an output device. A database for receiving results can be the
same as the input database or the historical database. An output device
can communicate over a network with a computer system of the invention.
The output device can be any device capable delivering processed results
to a user. Example output devices include, but are not limited to, a
telephone, a wireless telephone, a mobile phone, a PDA, a flash memory
drive, a light source, a sound generator, a fax machine, a computer, a
computer monitor, a printer, an iPOD, and a webpage.

[0249] An output of a computer system may assume any form, such as a
computer program, webpage, or print-out. Any other suitable
representation, picture, depiction or exemplification may be used.

[0250] Communication between devices or computer systems of the invention
can be any method of digital communication including, for example, over
the internet. Network communication can be wireless, ethernet-based,
fiber optic, or through fire-wire, USB, or any other connection capable
of communication as would be obvious to one skilled in the art. In an
embodiment, information transmitted by a system or method of the
invention can be encrypted by any method as would be obvious to one
skilled in the art. In the field of medicine, or diagnostics, encryption
may be necessary to maintain privacy of the data, as well as deter theft
of information.

[0251] It is further noted that the systems and methods may include data
signals conveyed via networks (for example, local area network, wide area
network, internet), fiber optic medium, carrier waves, wireless networks
for communication with one or more data processing or storage devices.
The data signals can carry any or all of the data disclosed herein that
is provided to or from a device.

[0252] Additionally, the methods and systems described herein may be
implemented on many different types of processing devices by program code
comprising program instructions that are executable by the device
processing subsystem. The software program instructions may include
source code, object code, machine code, or any other stored data that is
operable to cause a processing system to perform methods described
herein. Other implementations may also be used, however, such as firmware
or even appropriately designed hardware configured to carry out the
methods and systems described herein.

[0253] The methods herein may be packaged as a computer program product,
such as the expression of an organized set of instructions in the form of
natural or programming language statements that is contained on a
physical media of any nature (for example, written, electronic, magnetic,
optical or otherwise) and that may be used with a computer or other
automated data processing system of any nature (but preferably based on
digital technology). Such programming language statements, when executed
by a computer or data processing system, cause the computer system to act
in accordance with the particular content of the statements. Computer
program products include without limitation: programs in source and
object code and/or test or data libraries embedded in a computer readable
medium. Furthermore, the computer program product that enables a computer
system or data processing equipment device to act in preselected ways may
be provided in a number of forms, including, but not limited to, original
source code, assembly code, object code, machine language, encrypted or
compressed versions of the foregoing and any and all equivalents.

[0254] Information before, after, or during processing can be displayed on
any graphical display interface in communication with a computer system
(for example, a server). A computer system may be physically separate
from the instrument used to obtain values from the subject. In an
embodiment, a graphical user interface also may be remote from the
computer system, for example, part of a wireless device in communication
with the network. In another embodiment, the computer and the instrument
are the same device.

[0255] An output device or input device of a computer system can include
one or more user devices comprising a graphical user interface comprising
interface elements such as buttons, pull down menus, scroll bars, fields
for entering text, and the like as are routinely found in graphical user
interfaces known in the art. Requests entered on a user interface are
transmitted to an application program in the system (such as a Web
application). In one embodiment, a user of user device in the system is
able to directly access data using an HTML interface provided by Web
browsers and Web server of the system.

[0256] A graphical user interface may be generated by a graphical user
interface code as part of the operating system or server and can be used
to input data and/or to display input data. The result of processed data
can be displayed in the interface or a different interface, printed on a
printer in communication with the system, saved in a memory device,
and/or transmitted over a network. A user interface can refer to
graphical, textual, or auditory information presented to a user and may
also refer to the control sequences used for controlling a program or
device, such as keystrokes, movements, or selections. In another example,
a user interface may be a touch screen, monitor, keyboard, mouse, or any
other item that allows a user to interact with a system of the invention
as would be obvious to one skilled in the art.

[0257] In yet another aspect, a method of taking a course of medical
action by a user is provided including initiating a course of medical
action based on a posterior probability delivered from an output device
to said user.

[0258] The course of medical action can be delivering medical treatment to
said subject. The medical treatment can be selected from a group
consisting of the following: a pharmaceutical, surgery, organ resection,
and radiation therapy. The pharmaceutical can include, for example, a
chemotherapeutic compound for cancer therapy. The course of medical
action can include, for example, administration of medical tests, medical
imaging of said subject, setting a specific time for delivering medical
treatment, a biopsy, and a consultation with a medical professional.

[0259] The course of medical action can include, for example, repeating a
method described above.

[0260] A method can further include diagnosing the medical condition of
the subject by said user with said posterior probability from said output
device.

[0261] A system or method can involve delivering a medical treatment or
initiating a course of medical action. If a disease has been assessed or
diagnosed by a method or system of the invention, a medical professional
can evaluate the assessment or diagnosis and deliver a medical treatment
according to his evaluation. Medical treatments can be any method or
product meant to treat a disease or symptoms of the disease. In an
embodiment, a system or method initiates a course of medical action. A
course of medical action is often determined by a medical professional
evaluating the results from a processor of a computer system of the
invention. For example, a medical professional may receive output
information that informs him that a subject has a 97% probability of
having a particular medical condition. Based on this probability, the
medical professional can choose the most appropriate course of medical
action, such as biopsy, surgery, medical treatment, or no action. In an
embodiment, a computer system of the invention can store a plurality of
examples of courses of medical action in a database, wherein processed
results can trigger the delivery of one or a plurality of the example
courses of action to be output to a user. In an embodiment, a computer
system outputs information and an example course of medical action. In
another embodiment, the computer system can initiate an appropriate
course of medical action. For example, based on the processed results,
the computer system can communicate to a device that can deliver a
pharmaceutical to a subject. In another example, the computer system can
contact emergency personnel or a medical professional based on the
results of the processing. Courses of medical action a patient can take
include self-administering a drug, applying an ointment, altering work
schedule, altering sleep schedule, resting, altering diet, removing a
dressing, or scheduling an appointment and/or visiting a medical
professional. A medical professional can be for example a physician,
emergency medical personnel, a pharmacist, psychiatrist, psychologist,
chiropractor, acupuncturist, dermatologist, urologist, proctologist,
podiatrist, oncologist, gynecologist, neurologist, pathologist,
pediatrician, radiologist, a dentist, endocrinologist,
gastroenterologist, hematologist, nephrologist, ophthalmologist, physical
therapist, nutritionist, physical therapist, or a surgeon.

[0262] Medical professionals may take medical action when alerted by the
methods of the invention of the medical condition of a subject. Examples
of an alert include, but are not limited to, a sound, a light, a
printout, a readout, a display, an alarm, a buzzer, a page, an e-mail, a
fax alert, telephonic communication, or a combination thereof. The alert
may communicate to the user the raw subject data, the calculated
probability of the subject data.

[0263] The medical action can be based on rules imposed by the medical
professional or the computer system. Courses of medical action include,
but are not limited to, surgery, radiation therapy, chemotherapy,
prescribing a medication, evaluating mental state, delivering
pharmaceuticals, monitoring or observation, biopsy, imaging, and
performing assays and other diagnostic tests. In an embodiment, the
course of medical action may be inaction. Medical action also includes,
but is not limited to, ordering more tests performed on the patient,
administering a therapeutic agent, altering the dosage of an administered
therapeutic agent, terminating the administration of a therapeutic agent,
combining therapies, administering an alternative therapy, placing the
subject on a dialysis or heart and lung machine, performing computerized
axial tomography (CAT or CT) scan, performing magnetic resonance imaging
(MRI), performing a colonoscopy, administering a pain killer, prescribing
a medication. In some embodiments, the subject may take medical action.
For example, a diabetic subject may administer a dose of insulin.

[0264] FIG. 62 illustrates a method of delivering a probability that a
subject has a medical condition to a user and using the probability to
take a course of medical action. A blood sample is drawn from a patient
by a medical professional. In other embodiments, any method of obtaining
a biomarker values from a subject may be used as would be obvious to one
skilled in the art, such as swabs and urine tests. In FIG. 62, the sample
is assayed for a biomarker and biomarker values are generated. As
described herein, there may be many suitable methods for generating and
obtaining biomarker values. The values can then input into a computer by
a medical professional or other user, such as the subject or an
assistant. The data can then be processed by a method of the invention to
calculate the probability that a subject has the medical condition. An
output is generated and delivered to a user on a computer monitor, for
example, the output delivers the probability of a subject having a
medical condition and is display on a personal computer or laptop of the
subject's doctor. The output can also be delivered to the subject himself
or to a different medical professional. In another embodiment, the output
is delivered to a notification system, such as an alarm or another
computer-based program. In FIG. 62, based on the output, a physician can
take a medical action as described herein. In this example, the output
initiates a medical professional writing a prescription.

[0265]FIG. 63 illustrates a course of events related to the invention.
Data regarding a biomarker corresponding to a medical condition from a
patient are stored on a USB flash drive storage device. Data are input
into a computer system and data are processed by a calculation method of
the invention. For example, the computer system can be a server that
receives data from multiple input devices and can distribute results of a
calculation method to a plurality of output devices. In the example in
FIG. 63, the results of the calculation method are a probability that a
patient has a medical condition. The results delivered to the output
device can also be suggestions of courses of medical action, reports
based on the biomarker data, or warning or notification of the status of
the patient and/or calculation. FIG. 63 also demonstrates displaying a
probability of the medical condition of the subject on an output device
such as an iPOD. In this example, after reviewing the output, a user
decides the course of medical action is a patient needs to obtain an MR
image.

[0266]FIG. 64 illustrates another example practice of the invention. A
sample is taken from a patient by a syringe and the sample is analyzed
for a biomarker using a microscope to obtain a biomarker value
corresponding to a medical condition. Using a graphical user interface,
such as a website, a user can enter the results of the analysis into the
graphical user interface, or input device. The result of the biomarker
analysis is transmitted from an input device, such as a laptop computer
and the biomarker values are processed using a calculation method the
invention in a server of the invention. A probability of the subject from
which the biomarker values were obtained is output to a printout from a
printer to a user, such as the subject's physician. In this example, the
physician may take a course of medical action that comprises delivering a
medical treatment, such as performing an invasive surgical procedure,
such as a biopsy, based on results of the calculation.

Business Methods

[0267] In another aspect, a business method is disclosed that comprises:
receiving a first value of at least one biomarker of a subject;
calculating a first plurality of posterior probabilities of the
occurrence of a plurality of medical conditions of said subject with a
computer system using said a first value; delivering said first plurality
of posterior probabilities to a user; receiving a second value of at
least one biomarker of a subject and a result of a course of medical
action taken by said user based upon said delivery of said first
plurality of posterior probabilities; calculating a second plurality of
posterior probabilities of the occurrence of a plurality of medical
conditions of said subject with said computer system using said a second
value and said result of a course of the medical action; and delivering
said second plurality of posterior probabilities to said user. In an
embodiment, the first or second values are received from a user, such as
a user selected from the group consisting of the following: a physician,
a health care provider, a pharmacy, an insurance company, and the
subject. A first or second value can also be received from said user
through a webpage or an electronic device or an assay device.

[0268] In another embodiment, the first or second values are received from
a device, such as a device selected from the group consisting of the
following: a lab test device, a point-of-care assay device, a personal
electronic device, an electronic medical record, and a computer system.

[0269] Calculating can be carried out by a Monte Carlo engine and can be a
Bayesian statistical calculation.

[0270] In an embodiment, a plurality of medical conditions is at least
four medical conditions, for example from the group consisting of:
prostatitis due to inflammation, prostatitis due to infection, prostate
cancer, benign prostate hyperplasia, and no prostate disease. A biomarker
value can be from a PSA or fPSA assay.

[0271] A result of a course of medical action can be selected from the
group consisting of the following: a test result, a diagnosis, a cure, an
effect, and no effect. Posterior probabilities can be delivered to a user
through an electronic medical record or a webpage or an electronic device
with a display or a printout.

[0272] In an embodiment, the computer system comprises a processor, a
storage unit, and a device for network communication.

[0273] In an embodiment, a business method is carried out for a fee, for
example each delivery of posterior probabilities is carried out for a
fee.

[0274] A business method can further comprise suggesting a course of
medical action to said user based on said posterior probabilities, and
the suggestion can be provided for a fee.

[0275] In an embodiment of a business method of the invention, a posterior
probability of a medical condition is delivered to a user, wherein the
user, without limitation, is a patient, a medical person (such as a
physician), a health systems, or a lab. For example, a subject can have a
blood test that is assayed in a lab or at the point-of-care and then a
user sends the information from the assay to company (such as over the
internet), where the company performs processes or calculations with the
information and delivers an output (such as a probability of the
occurrence of a disease) to the user. The company can provide the output
for a fee.

[0276] In an embodiment, a business method comprises selling services of
the calculations and delivery of information directly to patients. For
example, the patients can use this information with their physicians.

[0277] In an embodiment, tokens can be sold to a user for a company to
perform a calculation method the invention and delivery of the results of
the calculation method to the user. For example, tokens can be sold
singly or in blocks of more than one. Further, each token can allow the
user to obtain one analysis as delivered by a business method of the
invention.

[0278] In another embodiment, a user of a token is a physician, insurance
company, or health system. In another embodiment of a method when a
subject has a periodically scheduled test, the results of the tests and
other patient information, including a history of biomarker results, can
be entered or uploaded to a computer system and then the company can
analyze the data and provide a report for use that includes probabilities
for one or more medical conditions, for example a doctor reading the
report with a patient.

[0279] A company can also use a method of the invention to sell services
to testing labs. For example, a token method as described herein can be
used. In an embodiment, labs may offer a package of services that include
blood draws, biomarker analysis and analysis as provided by the
invention.

[0280] In another embodiment, a company performs an analysis for insurance
company reimbursement.

[0281] Digital health services, such as WebMD, may offer a calculation
method of the invention as a value added service in conjunction with
medical information and other analysis services.

[0282] As new technology is developed to deliver blood test results at the
point of care within a short time, perhaps minutes, in an embodiment of
the invention the device doing the test can communicate to a computer
system wirelessly, through a docking station or other physical link or by
other means, including manual entry of the results. The computer system
can have software and/or a storage medium that receives the test results
and other information about the patient and for performing a calculation
method of the invention. A computer system can be on remote servers that
can process the new data along with other patient information already
stored in the system. Parallel processing can be used to analyze the data
and create a report quickly, perhaps in minutes. A report can be
transmitted to the computer in the doctor's office for viewing on screen
or for printing and use as hard copy. For example, the doctor may review
the report with the patient and decide on a course of medical action. For
example, the doctor and patient may decide on ultrasound imaging to
measure the volume of the patient's prostate. The prostate volume
measured can be entered into a computer system for further analysis that
can then create a new report that can be transmitted to the doctor's
display for viewing or printing. For example, the doctor may review the
new results with the patient and decide on a new course of medical
action. For example, the doctor and patient may decide to culture
prostate secretions for infection and start a course of antibiotics to
treat the possible infection.

[0283] In another embodiment with new technology developed to deliver
automated blood tests for a variety of biomarkers at one time, automated
protein profile equipment reports the levels of a wide variety of
proteins and other biomarkers in a sample. Biomarker values can be
automatically uploaded to a computer system as described herein and can
be added to other patient information already stored in the system. For
example, new probabilities can be calculated for all medical conditions
being considered. The doctor and/or the patient can consider the results
and choose appropriate courses of medical action.

[0284] Individuals can vary in their predisposition for various
conditions. In an embodiment, methods of the invention incorporate these
predispositions or risk factors into the prior probabilities of each
condition for each individual. For example, genetic testing might show a
man has a three times higher than normal risk of prostate cancer. Family
history or race might suggest other men have a two times higher than
normal risk of prostate cancer. Several risk factors can be combined into
an overall risk ratio that reflects a person increased or reduced risk of
a condition compared to an overall population. Risk factors can include
without limitation: gene profile, family history, race, obesity (BMI),
physical condition, geographic location of home and work over time, diet
and exercise regimen, exposure to environmental factors and other things.

[0285] An individual's future predisposition to various conditions can
depend on their past incidence of that condition and other related
conditions. In another embodiment, methods of the invention incorporate
these predispositions or risk factors into either the prior probabilities
of each condition for each individual or an explicit algorithm that may
be a Bayes process. For example, a man with a history of prostatitis
caused by infection has an increased risk of that condition in the
future. If a prior probability is adjusted then algorithms are used to
combine the risk factor based on past history with other risk factors
into an overall risk ratio that reflects a person increased or reduced
risk of a condition compared to an overall population. Alternatively, a
different algorithm can be used to calculate a new posterior probability
of a condition based on the details of the past history of that condition
and related conditions, perhaps using a Bayes process.

[0286] It is to be understood that the exemplary methods and systems
described herein may be implemented in various forms of hardware,
software, firmware, special purpose processors, or a combination thereof.
Preferably, a calculation method of the present invention is implemented
in software as an application program tangibly embodied on one or more
program storage devices. The application program may be executed by any
machine, device, or platform comprising suitable architecture. It is to
be further understood that, because some of the systems and methods
depicted in the Figures are preferably implemented in software, the
actual connections between the system components (or the process steps)
may differ depending upon the manner in which the method is programmed.
Given the teachings herein, one of ordinary skill in the related art will
be able to contemplate or practice these and similar implementations or
configurations of the present invention.

[0287] All the examples disclosed herein are to be considered
non-limiting. As an illustration, it should be understood that for the
processing flows described herein, the steps and the order of the steps
may be altered, modified, removed, and/or augmented and still achieve the
desired outcome.